Causal Inference Methods Research Articles

Practical Implementation of Advanced Causal Inference Method: Development of an R Package for Bayesian Marginal Structural Models with Time-Varying Treatment

Background: Observational studies offer a viable, efficient, and low-cost design to readily gather evidence on exposure effects. Although more practical, the exposure mechanism is non-randomized and causal inference methods are required to draw causal conclusions. Objectives: Bayesian approaches to causal inference have unique estimation features that are useful in many settings; however, there is a lack of open-access software packages to carry out these analyses. Our project seeks to address this gap by developing a user-friendly R package named “bayesmsm” for the implementation of the Bayesian Marginal Structural Models for longitudinal observational data with extensions to handle right-censoring which is common for longitudinal health data. This R package provides an elegant approach to conduct Bayesian causal inference with time-varying exposure and confounding. Methods: We use simulated datasets to assess the performance of the package and to illustrate its use. This package first implements Bayesian parametric treatment assignment weight estimation through Markov Chain Monte Carlo (MCMC) computation. It then uses Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect. The bootstrap calculation process was optimized for performance through parallel computing. The package has additional features, including handling right-censored data and generating analysis summaries and visualizations. Results: Using simulated datasets with and without right-censoring, the subject-specific treatment assignment weights were estimated using the package's weight estimation functions. The Bayesian posterior bootstrap results were further visualized by functions built within this package. Conclusions: The “bayesmsm” package provides a reliable tool to implement complex Bayesian Marginal Structural Model analysis. Future work will focus on extending this package to handle time-to-event data.

Econometrics of Climate Change Research

The econometrics of climate change explores the intricate interplay between economic activity and climate dynamics, offering empirical insights into the causes, impacts, and mitigation of global climate change. This research applies advanced econometric techniques to quantify the economic consequences of environmental changes and evaluate the effectiveness of climate policies. By leveraging panel data models, time-series analysis, and causal inference methods, this study investigates critical topics such as the economic costs of extreme weather events, the efficacy of carbon pricing mechanisms, and the role of renewable energy adoption in mitigating climate risks. Additionally, this research incorporates machine learning techniques to enhance predictive accuracy in modeling future climate-economic interactions. Through rigorous empirical analysis, the study provides actionable recommendations for policymakers to design sustainable strategies that balance economic growth with climate resilience. The findings contribute to a deeper understanding of the socioeconomic dimensions of climate change, emphasizing the importance of evidence-based decision-making in addressing one of humanity’s most pressing challenges.

Comparative analysis of manual vs. mechanical suturing techniques in esophagectomy: A propensity score‑matched study of long‑term outcomes.

Esophageal cancer, particularly esophageal squamous cell carcinoma (ESCC), is a major health concern worldwide, particularly in China. Surgical resection is still considered the primary curative treatment for this disease. However, the effect of different surgical methods-traditional hand-sewn anastomosis and modern mechanical anastomosis-remains controversial. A retrospective study was thus performed to elucidate how these two techniques affected the clinical prognosis of patients. Data were retrospectively collected from the comprehensive Esophageal Cancer Case Management Database of Sichuan Cancer Hospital and Institute (Chengdu, China), covering the period from 2010 to 2017. The cohort consisted of patients who underwent esophagectomy for ESCC, divided into two groups based on the suturing technique used: Manual suturing (MS) and mechanical suturing (MeS). A total of four causal inference methods for retrospective studies, namely inverse probability of treatment weighting, standardized mortality ratio weighting, overlap weighting and propensity score matching analysis, were used to minimize potential selection bias. The primary outcome evaluated was overall survival (OS), allowing for a direct comparison of the long-term efficacy of the two suturing methods. In a retrospective analysis of 2,510 patients undergoing esophagectomy, significant differences in OS were observed between the MeS group and the MS group (hazard ratio: 0.84; 95% confidence interval: 0.75-0.95; P=0.004). However, after matching or weighting based on causal inference analyses, no significant differences in survival outcomes between groups were obtained. The equivalence in outcomes suggests that either suturing method may be equally viable in clinical practice, offering flexibility in surgical decision-making without compromising OS.

Causal Inference in Natural Language Processing: Application Status and Future Outlook

Abstract. Causal reasoning has received much attention in recent years. Unlike statistical learning that focuses on the correlation between variables, causal inference can analyze the causality between variables and avoid false causal relationships caused by confounding factors. The use of causal inference to help solve Natural Language Processing (NLP) problems has made great progress. Most existing studies have focused on exploring the application of causal relationships in downstream tasks of NLP, achieving good results. This article conducted a comprehensive survey, focusing on investigating the advantages of causal inference methods in solving specific NLP problems and applications, as well as their practical advantages compared to deep learning methods. Intended to provide researchers in the NLP field with more detailed perspectives and recommendations. Specifically, this article first introduces the relevant concepts of causal inference, including causal inference and related concepts of causal relationships, as well as the differences between causal inference and statistical machine learning; Then, existing research on the use of causal relationships in downstream applications was summarized, including bias removal, stock price prediction, document dialogue based, and continuous few shot learning. Finally, a summary was made on the development of future causal relationships.

The causal effect of economic sanctions on political stability: A two‐stage difference‐in‐differences analysis

AbstractThis study employs the two‐stage difference‐in‐differences (2sDiD) estimator to investigate the causal effect of economic sanctions on political stability. It contributes to existing research by (1) re‐evaluating sanctions' impacts on political stability using newly introduced causal inference methods, and (2) distinguishing the effects of sanctions across various political regimes and economic globalisation levels. The article argues that economic sanctions create economic hardships for the target population, leading to public frustration toward their governments, which stimulates political mobilisation and thereby decreases the country's political stability. However, sanctions hurt democracies more than autocracies because autocratic regimes can suppress public dissent through repression and citizens face higher costs for opposition. Moreover, economic globalisation offers targets alternatives to sanctioned products and services, potentially weakening sanctions thus damaging political stability more in low‐globalised than in high‐globalised countries. Empirical findings from 9230 country‐year observations between 1949 and 2022 largely align with the theoretical predictions, showing that economic sanctions undermine the target's political stability, with these destabilising effects contingent upon its political regime and economic globalisation levels.

Linear and nonlinear causality in financial markets.

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper, we present a much more general framework for assessing co-dependencies by identifying linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management.

Integrating machine learning with causal inference for enhanced system dynamics modeling: A framework for predicting complex interactions

Approaches and methods like System Dynamics Modelling (SDM) have been significant for assessing the behavior of many systems. However, classical methodologies applied in traditional approaches to SDM fail to identify nonlinear feedback and power dependencies, revealing hidden temporal casual relationships. In this paper, I present a novel approach that combines ML and causal inference methods to improve the forecast capability and semantics of system dynamics models. Incorporating ML algorithms for predictions and Causal Inference techniques for explanation, this combined strategy presents a new era for understanding the system interaction and quantifying the hidden causes within various systems. We illustrate the advantages of the suggested framework over traditional SDM and purely ML approaches by employing it to analyze a genuine circumstance for both prognosis and discovering causal relationships. Our findings indicate that such integration is effective in enhancing the comprehension of system interactions and deriving a reliable method for estimating subsequent state conditions in complex contexts. The results are relevant for various disciplines, starting with economics and ending with environmental protection sciences, where interactions and changes vary. As a result, it will give a foundation for further studies of integrating future computerized methods in the dynamical system modeling of the next generation.

Methodological challenges and actionable recommendations in studying the health effects of high-concentration THC products.

In conducting a scoping review on the health effects of high-concentration cannabis products, we have uncovered pervasive methodological shortcomings within the cannabis literature. This paper begins by defining the 'causal effect' of interest for public health and delineating the desirable features of study design that can address crucial questions pertaining to public health and policy. We further delve into the methodological complexities inherent in studying the health effects of high-concentration cannabis products, describing challenges associated with the measurement of exposures and outcomes, confounding, selection bias, and the generalizability of findings. We introduce causal inference methods to mitigate potential biases in observational cannabis use studies. We identify specific areas that necessitate further development and investigation to deepen our understanding of this topic. Finally, this paper extends actionable recommendations, serving as a roadmap for upcoming research initiatives in this domain.

The effects of hypothetical psychological interventions on alcohol use in European young adults

Abstract Background Low psychosocial well-being (PWB) and high emotion-driven impulsiveness (EDI) are associated with alcohol. Yet, it is unclear whether a hypothetical intervention targeting one or the other in adolescence might be more effective in reducing alcohol consumption (AC) in young adulthood. Therefore, we aimed to compare the separate causal effects of PWB and EDI in adolescence on AC in young adulthood. Methods We included 505 European young adults from the IDEFICS/I.Family cohort (mean age: 20.2 years; age range: 18.2-23.5 years) who did not drink alcohol at study entry. AC was operationalized as the amount of weekly consumed alcoholic beverages (mean: 4.2 drinks per week; range: 0.3-70 drinks per week). EDI was assessed using the negative urgency subscale from the UPPS-P Impulsive Behaviour Scale. PWB was assessed using the KINDLR Health-Related Quality of Life Questionnaire. Following the principles of target trial emulation, we estimated, separately, the average causal effects of PWB and EDI on AC accounting for relevant confounders and applying a semi-parametric doubly robust method (targeted maximum likelihood estimation). We stratified the results by sex and parental education. Results If all adolescents, hypothetically, had high levels of PWB, compared to low levels, we estimated a decrease in the average amount of alcoholic beverages in young adulthood by 0.1 drinks per week [95%-confidence interval: -2.3; 2.1]. Furthermore, if all adolescents had low levels of EDI, compared to high levels, we estimated an increase in alcoholic beverages in young adulthood by 1.5 drinks per week [0.1; 2.9]. Different effects for sex and parental education groups were found. Conclusions Hypothetical interventions targeting PWB in adolescents were not found to have effects on reducing AC in young adulthood. Interventions targeting EDI, however, would lead to an increase in AC. This may be due to unmeasured confounding and the missing distinction between drinking motives. Key messages • We demonstrate that causal inference methods, compared to traditional ones, improve the robustness of estimated effect measures and address important sources of bias in European cohort data. • To inform public health interventions on reducing alcohol consumption, future research should investigate different drinking motives, e.g. alleviating negative emotions or enhancing positive emotions.

Framework for approaching federated causal inference based on real-world observational data sources

Abstract Context Causal inference methods, generally requiring detailed individual information, allow us to evaluate public health interventions in real-world observational settings. When comparing interventions across borders and for this purpose leveraging distributed sensitive individual-level data, a federated approach can be aided. However, reusing such distributed data while maintaining privacy and interoperability, is a challenging endeavor. Methodological framework In the context of the BY-COVID project, a methodological framework to approach federated causal inference was published in October 2023. It is presented as a set of guidelines in the form several consecutive steps, starting with defining a research question, to building a causal model, translating the causal model into data requirements captured within a Common Data Model (CDM), generating synthetic data complying with the requirements captured in the CDM, and developing interoperable analysis scripts which can be distributed for deployment in different federated nodes. The proposed framework was built through implementing and combining existing methodologies and taking into account the principles of legal, organizational, semantic and technical interoperability. Objectives This presentation will provide an overview of the proposed framework, and its demonstration by performing an international comparison of the real-world effectiveness of SARS-CoV-2 primary vaccination campaigns in preventing infections at different federated sites. Challenges encountered (e.g. difficulties in complying with data requirements by linking heterogeneous local data sources) and lessons learned in the course of the demonstrator will be highlighted. Implications We aim to contribute to a public health research field where the rapid assessment of emerging population-health research questions (potentially feeding into evidence-based policy-making) is achievable, in that way contributing to European pandemic preparedness.

Effect of four hemoglobin transfusion threshold strategies in patients with acute myocardial infarction and anemia: a target trial emulation using MINT trial data

Abstract Background The hemoglobin (Hgb) threshold at which red blood cell transfusion affords greatest benefit, without increasing transfusion-associated risks, is uncertain for patients with acute myocardial infarction (MI). The Myocardial Ischemia and Transfusion (MINT) trial recently showed a restrictive transfusion strategy (Hgb threshold <7-8g/dL) resulted in a 15% increased risk of 30-day all-cause death or recurrent MI (death/MI) compared with a liberal strategy (Hgb threshold <10g/dL) in 3,504 adults with acute MI and anemia. However, the transfusion effect at different Hgb thresholds remains uncertain. Purpose To better understand the gradient of effects from Hgb threshold transfusion triggers between 7g/dL and 10g/dL. We estimated the effects of four Hgb transfusion thresholds on 30-day death/MI and death in patients with acute MI and anemia. Methods We used data from the MINT trial and applied an innovative causal inference method, target trial emulation, to assess four Hgb thresholds. We emulated a hypothetical, 4-arm target trial of <10g/dL, <9g/dL, <8g/dL, and <7g/dL transfusion strategies in adults with acute MI and Hgb <10g/dL using a "cloning" procedure. The target trial protocol closely mirrored that of the MINT trial. We conducted a per-protocol analysis of the emulated target trial using censoring and inverse probability (IP) weighting; we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) by fitting pooled logistic regression models with a time-dependent intercept for time free of the outcomes. We estimated IP weighted Kaplan-Meier cumulative incidence curves under each strategy for 30-day outcomes. Results Of the MINT trial participants, 3,492 (99.7%) met eligibility criteria for the hypothetical target trial of four transfusion strategies. The unadjusted rate of 30-day death/MI was lowest in the <10g/dL strategy and highest in the <9g/dL strategy, and the rate of transfusions decreased monotonically as Hgb thresholds decreased. Relative to the <10g/dL strategy, the estimated HRs (95% CI) from the IP weighted model of 30-day death/MI were 1.07 (0.77-1.50) for the <9g/dL strategy, 1.14 (0.84-1.55) for the <8g/dL strategy, and 1.44 (0.99-2.09) for the <7g/dL strategy (Figure). A log-linear estimate over strategy thresholds between 7-10g/dL estimated a 1.12 (95% CI 1.00-1.27) times greater average risk of death/MI and a 1.12 (95% CI 0.92, 1.37) times greater average risk of death over 30 days for each 1g/dL decrease in the Hgb threshold. The IP weighted cumulative incidence curves showed similar increase in risk of death/MI as transfusion thresholds decreased throughout 30 days post-randomization. Conclusions Relative to a <10g/dL transfusion strategy, we estimated a progressively greater risk of 30-day death/MI as transfusion Hgb thresholds decreased within the range of 7-10g/dL. Targeting a Hgb concentration of 10g/dL in patients with a recent acute MI may avoid serious risks associated with anemia.Hazard ratios for 30-day death/MI

Nonlinear mixed-effects modeling as a method for causal inference to predict exposures under desired within-subject dose titration schemes.

The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed-effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed-effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed-effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed-effects model. As an example, we use a simulated clinical trial with within-subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained.

Evaluating Natural Course Performance in Parametric G-formula: Review of Current Practice and Illustration Based on the United Autoworkers-General Motors Cohort.

The parametric g-formula is a causal inference method that appropriately adjusts for time-varying confounding affected by prior exposure. Like all parametric methods, it assumes correct model specification, usually assessed by comparing the observed outcome with the simulated outcome under no intervention (natural course). However, it is unclear how to evaluate natural course performance and whether other variables should also be considered. We reviewed current practices for evaluating model misspecification in applications of parametric g-formula. To illustrate the pitfalls of current practices, we then applied the parametric g-formula to examine cardiovascular disease mortality in relation to occupational exposure in the United Autoworkers-General Motors cohort (UAW-GM), comparing 20 parametric model sets and qualitatively assessing natural course performance for all time-varying variables over follow-up. We found that current practices of evaluating model misspecification are often insufficient, increasing risk of bias and statistical cherry picking. Based on our motivational analyses of the UAW-GM cohort, good natural course performance of the outcome does not guarantee good simulations of other covariates; poor predictions of exposures and covariates may still exist. We recommend reporting natural course performance for all time-varying variables at all time-points. Objective criteria for evaluating model misspecification in parametric g-formula need to be developed.

Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time

Background: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time. Methods: For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databases, we propose using restricted mean survival time or restricted mean time lost, which are applicable even in competing risk situations. We demonstrate their application by assessing the compliance of patients with acute myocardial infarction (AMI) to high-power statins over 12 months in Austria’s political districts, using pseudo-observations and employing causal inference methods to achieve regional comparability. Results: We analyzed the compliance of 31,678 AMI patients from Austria’s 116 political districts with index AMI between 2011 and 2015. The results revealed considerable compliance variations across districts but also plausible spatial similarities. Conclusions: Restricted mean survival time and restricted mean time lost provide interpretable estimates of patients’ expected time in compliance (lost), well-suited for risk-adjusted entity comparisons in the presence of (measurable) confounding, censoring, and competing risks.

Gut microbiota causally impacts adrenal function: a two-sample mendelian randomization study

Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002–0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004–0.013, P = 2 × 10− 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

What Explains Hop Growers' Fungicide Use Intensity and Management Costs in Response to Powdery Mildew?

Methods for causal inference from observational data are common in human disease epidemiology and social sciences but are used relatively little in plant pathology. We draw upon an extensive data set of the incidence of hop plants with powdery mildew (caused by Podosphaera macularis) collected from yards in Oregon from 2014 to 2017 and associated metadata on grower cultural practices, cultivar susceptibility to powdery mildew, and pesticide application records to understand variation in and causes of growers' fungicide use and associated costs. An instrumental causal forest model identified growers' spring pruning thoroughness, cultivar susceptibility to two of the dominant pathogenic races of P. macularis, network centrality of yards during May-June and June-July time transitions, and the initial strain of the fungus detected as important variables determining the number of pesticide active constituents applied by growers and the associated costs they incurred in response to powdery mildew. Exposure-response function models fit after covariate weighting indicated that both the number of pesticide active constituents applied and their associated costs scaled linearly with the seasonal mean incidence of plants with powdery mildew. Although the causes of pesticide use intensity are multifaceted, biological and production factors collectively influence the incidence of powdery mildew, which has a direct exposure-response relationship with the number of pesticide active constituents that growers apply and their costs. Our analyses point to several potential strategies for reducing pesticide use and costs for management of powdery mildew on hop. We also highlight the utility of these methods for causal inference in observational studies.

Deep‐Learning Based Causal Inference: A Feasibility Study Based on Three Years of Tectonic‐Climate Data From Moxa Geodynamic Observatory

AbstractHighly sensitive laser strainmeters at Moxa Geodynamic Observatory (MGO) measure motions of the upper Earth's crust. Since the mountain overburden of the laser strainmeters installed in the gallery of the observatory is relatively low, the recorded time series are strongly influenced by local meteorological phenomena. To estimate the nonlinear effect of the meteorological variables on strain measurements in a non‐stationary environment, advanced methods capable of learning the nonlinearity and discovering causal relationships in the non‐stationary multivariate tectonic‐climate time series are needed. Methods for causal inference generally perform well in identifying linear causal relationships but often struggle to retrieve complex nonlinear causal structures prevalent in real‐world systems. This work presents a novel model invariance‐based causal discovery (CDMI) method that utilizes deep networks to model nonlinearity in a multivariate time series system. We propose to use the theoretically well‐established Knockoffs framework to generate in‐distribution, uncorrelated copies of the original data as interventional variables and test the model invariance for causal discovery. To deal with the non‐stationary behavior of the tectonic‐climate time series recorded at the MGO, we propose a regime identification approach that we apply before causal analysis to generate segments of time series that possess locally consistent statistical properties. First, we evaluate our method on synthetically generated time series by comparing it to other causal analysis methods. We then investigate the hypothesized effect of meteorological variables on strain measurements. Our approach outperforms other causality methods and provides meaningful insights into tectonic‐climate causal interactions.

Statistical challenges and solutions in multidisciplinary clinical research: Bridging the gap between

This paper delves into the intricate challenges and innovative solutions in applying statistical methodologies within clinical research, aiming to bridge the gap between biostatistics and medicine. The study meticulously examines fundamental biostatistical concepts, addressing the complexities of modern clinical trials and observational studies. Through a comprehensive review of advanced regression models, causal inference techniques, and machine learning algorithms, the paper illuminates the evolving landscape of biostatistics in handling high-dimensional data and confounding variables. The methods employed in this study involve an extensive analysis of current literature, case studies, and practical applications that demonstrate the utility of these advanced methodologies. Key findings reveal that traditional statistical approaches often fall short in capturing the complexities of clinical data, necessitating the adoption of more sophisticated techniques. The integration of non-linear regression models, robust causal inference methods, and machine learning has significantly enhanced the accuracy and reliability of research outcomes, offering deeper insights into patient outcomes and treatment efficacy. Conclusions drawn from this study underscore the critical need for a paradigm shift in clinical research, moving beyond the rigid reliance on p-values towards a more holistic approach that emphasizes effect sizes, confidence intervals, and practical significance. The paper recommends continued innovation in statistical methodologies, particularly the integration of big data analytics and machine learning, to address the growing complexities of biomedical data. Furthermore, it advocates for interdisciplinary collaboration and ethical considerations in the application of these advanced techniques to ensure that biostatistics continues to contribute meaningfully to the advancement of medical science.

Exposing disparities in flood adaptation for equitable future interventions in the USA

As governments race to implement new climate adaptation solutions that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether they benefit all communities. Using the USA as an example, we illustrate the importance of considering such disparities for flood adaptation through a FEMA dataset of ~ 2.5 million flood insurance claims. We use CausalFlow, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community’s income, racial demographics, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000–15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts. Even among low-income communities, savings are >$6,000 per household higher in predominantly white communities. Future flood adaptation efforts should go beyond reducing losses overall and aim to equitably support communities in the race for climate adaptation.

Mapping biological influences on the human plasma proteome beyond the genome

Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10–77% of the variation in plasma (median 19.88%, interquartile range 14.01–31.09%), independent of technical factors (median 2.48%, interquartile range 0.78–6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein–disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.