Potential Outcomes Framework Research Articles

Behavioral carry-over effect and power consideration in crossover trials.

A crossover trial is an efficient trial design when there is no carry-over effect. To reduce the impact of the biological carry-over effect, a washout period is often designed. However, the carry-over effect remains an outstanding concern when a washout period is unethical or cannot sufficiently diminish the impact of the carry-over effect. The latter can occur in comparative effectiveness research, where the carry-over effect is often non-biological but behavioral. In this paper, we investigate the crossover design under a potential outcomes framework with and without the carry-over effect. We find that when the carry-over effect exists and satisfies a sign condition, the basic estimator underestimates the treatment effect, which does not inflate the type I error of one-sided tests but negatively impacts the power. This leads to a power trade-off between the crossover design and the parallel-group design, and we derive the condition under which the crossover design does not lead to type I error inflation and is still more powerful than the parallel-group design. We also develop covariate adjustment methods for crossover trials. We evaluate the performance of cross-over design and covariate adjustment using data from the MTN-034/REACH study.

NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation

Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network (NN) architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER), a generalized method for causal effect estimation. NESTER integrates the ideas used in existing methods based on multi-head NNs for causal effect estimation into one framework. We design a Domain Specific Language (DSL) tailored for causal effect estimation based on causal inductive biases used in literature. We conduct a theoretical analysis to investigate NESTER's efficacy in estimating causal effects. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.

Causal Effects of Time-Varying Exposures: A Comparison of Structural Equation Modeling and Marginal Structural Models in Cross-Lagged Panel Research

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework.

Causal inference for psychologists who think that causal inference is not for them

AbstractCorrelation does not imply causation and psychologists' causal inference training often focuses on the conclusion that therefore experiments are needed—without much consideration for the causal inference frameworks used elsewhere. This leaves researchers ill‐equipped to solve inferential problems that they encounter in their work, leading to mistaken conclusions and incoherent statistical analyses. For a more systematic approach to causal inference, this article provides brief introductions to the potential outcomes framework—the “lingua franca” of causal inference—and to directed acyclic graphs, a graphical notation that makes it easier to systematically reason about complex causal situations. I then discuss two issues that may be of interest to researchers in social and personality psychology who think that formalized causal inference is of little relevance to their work. First, posttreatment bias: In various common scenarios (noncompliance, mediation analysis, missing data), researchers may analyze data from experimental studies in a manner that results in internally invalid conclusions, despite randomization. Second, tests of incremental validity: Routine practices in personality psychology suggest that they may be conducted for at least two different reasons (to demonstrate the non‐redundancy of new scales, to support causal conclusions) without being particularly suited for either purpose. Taking causal inference seriously is challenging; it reveals assumptions that may make many uncomfortable. However, ultimately it is a necessary step to ensure the validity of psychological research.

Estimation of the number needed to treat, the number needed to be exposed, and the exposure impact number with instrumental variables

Abstract Objectives The Number Needed to Treat (NNT) is an efficacy index defined as the average number of patients needed to treat to attain one additional treatment benefit. In observational studies, specifically in epidemiology, the adequacy of the populationwise NNT is questionable since the exposed group characteristics may substantially differ from the unexposed. To address this issue, groupwise efficacy indices were defined: the Exposure Impact Number (EIN) for the exposed group and the Number Needed to be Exposed (NNE) for the unexposed. Each defined index answers a unique research question since it targets a unique sub-population. In observational studies, the group allocation is typically affected by confounders that might be unmeasured. The available estimation methods that rely either on randomization or the sufficiency of the measured covariates for confounding control result in statistically inconsistent estimators of the true EIN, NNE, and NNT. This study presents a theoretical framework for statistically consistent point and interval estimation of the NNE, EIN and NNE in observational studies with unmeasured confounders. Methods Using Rubin’s potential outcomes framework, this study explicitly defines the NNT and its derived indices, EIN and NNE, as causal measures. Then, we use instrumental variables to introduce a novel method to estimate the three aforementioned indices in observational studies where the omission of unmeasured confounders cannot be ruled out. To illustrate the novel methods, we present two analytical examples – double logit and double probit models. Next, a corresponding simulation study and a real-world data example are presented. Results This study provides an explicit causal formulation of the EIN, NNE, and NNT indices and a comprehensive theoretical framework for their point and interval estimation using the G-estimators in observational studies with unmeasured confounders. The analytical proofs and the corresponding simulation study illustrate the improved performance of the new estimation method compared to the available methods in terms of consistency and the confidence intervals empirical coverage rates. Conclusions In observational studies, traditional estimation methods to estimate the EIN, NNE, or NNT result in statistically inconsistent estimators. We introduce a novel estimation method that overcomes this pitfall. The novel method produces consistent estimators and reliable CIs for the true EIN, NNE, and NNT. Such a method may facilitate more accurate clinical decision-making and the development of efficient public health policies.

Bounds for selection bias using outcome probabilities

Abstract Objectives Determining the causal relationship between exposure and outcome is the goal of many observational studies. However, the selection of subjects into the study population, either voluntary or involuntary, may result in estimates that suffer from selection bias. To assess the robustness of the estimates as well as the magnitude of the bias, bounds for the bias can be calculated. Previous bounds for selection bias often require the specification of unknown relative risks, which might be difficult to provide. Here, alternative bounds based on observed data and unknown outcome probabilities are proposed. These unknown probabilities may be easier to specify than unknown relative risks. Methods I derive alternative bounds from the definitions of the causal estimands using the potential outcomes framework, under specific assumptions. The bounds are expressed using observed data and unobserved outcome probabilities. The bounds are compared to previously reported bounds in a simulation study. Furthermore, a study of perinatal risk factors for type 1 diabetes is provided as a motivating example. Results I show that the proposed bounds are often informative when the exposure and outcome are sufficiently common, especially for the risk difference in the total population. It is also noted that the proposed bounds can be uninformative when the exposure and outcome are rare. Furthermore, it is noted that previously proposed assumption-free bounds are special cases of the new bounds when the sensitivity parameters are set to their most conservative values. Conclusions Depending on the data generating process and causal estimand of interest, the proposed bounds can be tighter or wider than the reference bounds. Importantly, in cases with sufficiently common outcome and exposure, the proposed bounds are often informative, especially for the risk difference in the total population. It is also noted that, in some cases, the new bounds can be wider than the reference bounds. However, the proposed bounds based on unobserved probabilities may in some cases be easier to specify than the reference bounds based on unknown relative risks.

Multiply robust estimators in longitudinal studies with missing data under control-based imputation.

Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves n1/2-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than n-1/4 when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.

A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study

The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example.

Prospective and retrospective causal inferences based on the potential outcome framework

Prospective and retrospective causal inferences based on the potential outcome framework

Identifying culture as cause: Challenges and opportunities.

Causal inference lies at the core of many scientific endeavours. Yet answering causal questions is challenging, especially when studying culture as a causal force. Against this backdrop, this paper reviews research designs and statistical tools that can be used - together with strong theory and knowledge about the context of study - to identify the causal impact of culture on outcomes of interest. We especially discuss how overlooked strategies in cultural evolutionary studies can allow one to approximate an ideal experiment wherein culture is randomly assigned to individuals or entire groups (instrumental variables, regression discontinuity design, and epidemiological approach). In doing so, we also review the potential outcome framework as a tool to engage in causal reasoning in the cultural evolutionary field.

The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization

Recent developments in the causal-inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do researchers actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential-outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and noninterference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity-score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the dependent variable is modeled as a function of both controls and the independent variable), and doubly robust estimators (which combine models for both independent and dependent variables). A companion R Notebook is available at github.com/ArthurChatton/CausalCookbook. We hope that this nontechnical introduction not only helps psychologists and other social scientists expand their causal toolbox but also facilitates communication across disciplinary boundaries when it comes to causal inference, a research goal common to all fields of research.

Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions

We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We offer algorithms for inference on the causal parameters that are aggregated over the true population distribution of the covariates. We show the usefulness of our approach by studying three empirical examples: the benefit of attending private school for entering a prestigious university in Pakistan; the relationship between staying in school and getting involved with drug-trafficking gangs in Brazil; and the link between physicians’ hours and size of the group practice in the United States.

Mortality analysed by propensity score matching: an application to national neonatal audit

Mortality, a key outcome variable in many population studies and studies of healthcare and its interventions, is commonly analysed by regression of the survival status on a set of relevant background variables. We describe an alternative based on the potential outcomes framework, in which we ask how a particular group of subjects, or a population, whose outcomes were realised in one condition, would have fared had they been treated or cared for in different circumstances. The method is applied to neonatal mortality in the operational delivery networks in England and Wales. The performance of a network is assessed by the difference of the mortality rates of the network and of a matched set of babies drawn from the entire domain of the study. The outlier status of a network is established by a decision-theoretical approach.

Defining the Need for Causal Inference to Understand the Impact of Social Determinants of Health: A Primer on Behalf of the Consortium for the Holistic Assessment of Risk in Transplantation (CHART).

This study aims to introduce key concepts and methods that inform the design of studies that seek to quantify the causal effect of social determinants of health (SDOH) on access to and outcomes following organ transplant. The causal pathways between SDOH and transplant outcomes are poorly understood. This is partially due to the unstandardized and incomplete capture of the complex interactions between patients, their neighborhood environments, the tertiary care system, and structural factors that impact access and outcomes. Designing studies to quantify the causal impact of these factors on transplant access and outcomes requires an understanding of the fundamental concepts of causal inference. We present an overview of fundamental concepts in causal inference, including the potential outcomes framework and direct acyclic graphs. We discuss how to conceptualize SDOH in a causal framework and provide applied examples to illustrate how bias is introduced. There is a need for direct measures of SDOH, increased measurement of latent and mediating variables, and multi-level frameworks for research that examine health inequities across multiple health systems to generalize results. We illustrate that biases can arise due to socioeconomic status, race/ethnicity, and incongruencies in language between the patient and clinician. Progress towards an equitable transplant system requires establishing causal pathways between psychosocial risk factors, access, and outcomes. This is predicated on accurate and precise quantification of social risk, best facilitated by improved organization of health system data and multicenter efforts to collect and learn from it in ways relevant to specialties and service lines.

Estimating average treatment effects for clustered RCTs with recruitment bias.

In clustered randomized controlled trials (RCTs), sample recruitment is often conducted after cluster randomization. This timing can lead to recruitment bias if access to the intervention affects the composition of study-eligible cluster entrants and study consenters. This article develops a potential outcomes framework in such settings that yields a causal estimand that pertains to the always-recruited in either research condition. A consistent inverse probability weighting (IPW) estimator is developed using data on recruits only, and a generalized estimating equations approach is used to obtain robust clustered SE estimators that adjust for estimation error in the IPW weights. A simple data collection strategy is discussed to improve the predictive accuracy of the logit propensity score models. Simulations show that the IPW estimator achieves nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from an RCT testing the effects of a behavioral health intervention in schools. An R program for estimation is available for download.

Crash modification factors of rumble strips on horizontal curves of two-lane rural roads: A propensity scores potential outcomes approach

Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips.

Tutorial on causal mediation analysis with binary variables: An application to health psychology research.

Mediation analysis has been widely applied to explain why and assess the extent to which an exposure or treatment has an impact on the outcome in health psychology studies. Identifying a mediator or assessing the impact of a mediator has been the focus of many scientific investigations. This tutorial aims to introduce causal mediation analysis with binary exposure, mediator, and outcome variables, with a focus on the resampling and weighting methods, under the potential outcomes framework for estimating natural direct and indirect effects. We emphasize the importance of the temporal order of the study variables and the elimination of confounding. We define the causal effects in a hypothesized causal mediation chain in the context of one exposure, one mediator, and one outcome variable, all of which are binary variables. Two commonly used and actively maintained R packages, mediation and medflex, were used to analyze a motivating example. R code examples for implementing these methods are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Causal moderated mediation analysis: Methods and software.

Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and contextual characteristics. Various moderated mediation analysis methods have been developed under the traditional path analysis/structural equation modeling framework. One challenge is that the definitions of moderated mediation effects depend on statistical models of the mediator and the outcome, and no solutions have been provided when either the mediator or the outcome is binary, or when the mediator or outcome model is nonlinear. In addition, it remains unclear to empirical researchers how to make causal arguments of moderated mediation effects due to a lack of clarifications of the underlying assumptions and methods for assessing the sensitivity to violations of the assumptions. This article overcomes the limitations by developing general definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. We also developed a user-friendly R package moderate.mediation ( https://cran.r-project.org/web/packages/moderate.mediation/index.html ) that allows applied researchers to easily implement the proposed methods and visualize the initial analysis results and sensitivity analysis results. We illustrated the application of the proposed methods and the package implementation with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

Social media and e-cigarette use: The mediating role of mental health conditions

BackgroundIn recent years, youth social media use, youth e-cigarette use, and youth mental health conditions have become major public health concerns. We examined the role of mental health condition indicators in the link between youth social media use and e-cigarette use. MethodsWe used data from the 2022 National Youth Tobacco Survey (N = 23,445) to measure social media use by time spent, anxiety and depression symptoms by the Patient Health Questionnaire-4, and past-30-day e-cigarette use. We applied the potential outcomes framework to examine the relationship between social media use and e-cigarette use mediated by mental health condition indicators. ResultsMental health condition indicators mediated the relationship between social media use and e-cigarette use in a dose-response manner, with higher levels of social media use associated with higher odds of e-cigarette use. With mental health condition indicators as a mediator, those who used social media for 4+ hours/day or 3–4 h/day had significantly higher odds of e-cigarette use than those who used social media sometimes or never. In the sex-stratified analysis, we found that very often social media use was associated with higher odds of e-cigarette use for both males and females, compared to sometimes users (OR = 1.53, 95 % CI [1.18,1.96] for males; OR = 2.27, 95 % CI [1.76,2.92] for females). LimitationsAnxiety and depression symptoms were self-reported. ConclusionsThis study shows that anxiety and depression symptoms mediate the relationship between social media and e-cigarette use among youth and support the growing concern that frequent social media use may negatively affect youth health.

Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield. However, the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques. Facing with the challenge, a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval (IR) model and a Hybrid Intention Recognition (HIR) model. The target data acquired by the sensors are modelled as Basic Probability Assignments (BPAs) based on evidence theory to create uncertain datasets. Then, the HIR model is utilized to recognize intent for a tested sample from uncertain datasets. Finally, the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample. Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes. The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.