Causal Inference Framework Research Articles

Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

Association between TB delay and TB treatment outcomes in HIV-TB co-infected patients: a study based on the multilevel propensity score method.

HIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors. We conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW). The successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings. HIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment.

Application of the target trial emulation framework to external comparator studies

External comparator (EC) studies are increasingly being used to generate evidence that supports the evaluation of emerging pharmacological treatments for regulatory and health technology assessment (HTA) purposes. However, the reliability of evidence generated from EC studies can vary. In this paper, we outline how an existing framework for causal inference, the target trial emulation (TTE) framework, can be appropriately applied to improve the design and analysis of EC studies. Applying the TTE framework involves specifying the protocol of an ideal target trial which would answer the causal question of interest, then emulating its key elements under real-world (RW) settings. We describe each component of the original TTE framework and explain how it can be applied to EC studies, supplemented with practical recommendations. We also highlight special considerations and limitations in applying the TTE framework to EC studies. We describe how the TTE framework can be applied to improve the clarity, transparency, and reliability of evidence generated from EC studies.

Estimating treatment effect in randomized trial after control to treatment crossover using external controls

ABSTRACT In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.

Phototrophic Nitrogen Fixation, a Neglected Biogeochemical Process in Mine Tailings?

Biological nitrogen fixation (BNF) has important ecological significance in mine tailing by contributing to the initial accumulation of nitrogen. In addition to chemolithotrophic and heterotrophic BNF, light may also fuel BNF in oligotrophic mine tailings. However, knowledge regarding the occurrence and ecological significance of this biogeochemical process in mine tailings remains ambiguous. The current study observed phototrophic BNF in enrichment cultures established from three primary successional stages (i.e., original tailings, biological crusts, and pioneer plants) of tailings. Notably, phototrophic BNF in tailings may be more active at vegetation stages (i.e., biological crusts and pioneering plants) than in bare tailings. DNA-stable isotope probing identified Roseomonas species as potential aerobic anoxygenic phototrophs responsible for phototrophic BNF. Furthermore, metagenomic binning as well as genome mining revealed that Roseomonas spp. contained essential genes involved in nitrogen fixation, anoxygenic photosynthesis, and carbon fixation, suggesting their genetic potential to mediate phototrophic BNF. A causal inference framework equipped with the structural causal model suggested that the enrichment of putative phototrophic diazotrophic Roseomonas may contribute to an elevated total nitrogen content during primary succession in these mine tailings. Collectively, our findings suggest that phototrophic diazotrophs may play important roles in nutrient accumulation and hold the potential to facilitate ecological succession in tailings.

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure.

Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. We also show that adjusting for so-called prognostic variables that are independent of true exposure and measurement error in the outcome model, may increase efficiency, while adjusting for any covariates that are associated only with true exposure generally results in efficiency loss in realistic settings. We apply the proposed covariate selection approach to the Health Professional Follow-up Study dataset to study the effect of fiber intake on cardiovascular disease. Finally, we extend the originally proposed estimators to a nonparametric setting where effect modification by covariates is allowed.

Impact of DREAMS interventions on attitudes towards gender norms among adolescent girls and young women: Findings from a prospective cohort in Kenya.

The DREAMS partnership aims to deliver a comprehensive package to reduce HIV incidence among adolescent girls and young women (AGYW), including through shifting gender norms. We evaluate DREAMS' effect on attitudes towards gender norms in two Kenyan settings. AGYW aged 15-22 in Nairobi (n = 852) and Gem (n = 761) were randomly selected for cohort enrolment in 2017-18 and followed-up to 2019. We described the proportion of AGYW and their male peers with equitable attitudes towards gender norms, using an adapted version of the GEM scale. We estimated the association between self-reported invitation to DREAMS (in 2017-18) and AGYW's attitudes towards two dimensions of gender norms, and then applied a causal inference framework to estimate the difference in the proportion of AGYW with equitable attitudes under the counterfactual scenarios that all versus none were DREAMS beneficiaries. We estimated that overall, 90.2% versus 87.1% of AGYW would have equitable norms around sexual and reproductive health decision-making in Nairobi if all versus none were DREAMS beneficiaries (+3.1; 95%CI:-2.5, +9.0). In Gem, we estimated a risk difference of +1.0 (89.6% vs 88.6%, 95%CI: -3.6,+5.6). There was no evidence for an effect of DREAMS on attitudes towards violence-related norms (Nairobi: 82.7% vs 82.2%, +0.5; 95%CI: -5.3,+6.5; Gem: 44.3% vs 48.2%, -3.9; 95%CI: -11.7,+3.0). We found no evidence of an impact of DREAMS invitation on individual attitudes towards gender norms. In some cases, equitable attitudes at enrolment left limited scope for improvement, and additional effort may be required to shift inequitable violence attitudes among both AGYW and their male peers.

A Discrimination-Free Premium under a Causal Framework

We examine the discrimination-free premium in Lindholm et al. within a theoretical causal inference framework, and we consider its societal context to assess when the pricing formula should be used. We consider the insurance pricing problem through the use of directed acyclic graphs. This particular tool allows us to rigorously define an insurance risk factor in a causal framework. We then use this definition in assessing the appropriate application of the discrimination-free premium through three simplified pricing examples, including a health insurance policy and two personal automobile insurance policies with different coverages. From our findings, we suggest criteria for the application of the discrimination-free premium that is dependent on the risk factors and the social context.

Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China

Large-scale research infrastructures (LSRIs) are widely acknowledged as a crucial instrument for venturing into the uncharted territories of science and technology, as well as contributing to the well-being of society. However, only a limited number of literature have scrutinized the impact of LSRIs, founded upon a causal inference framework. Moreover, the function of LSRIs in the advancement of innovation at the regional level remains inadequately identified. Drawing on the resource-based view, this study develops a conceptual framework that links the scientific effect of LSRIs to innovation resources in order to assess their impact on knowledge innovation (KI). Taking China’s National Supercomputing Center (NSC) as a case, three major mechanism hypotheses are proposed for the impact of NSC on KI, including basic effect, network effect, and technology effect. Using panel data from 283 cities in China from 2000 to 2020, we employ a spatial difference-in-differences estimation model to examine the impact of NSC on KI. The research finds that: (1) The construction of NSC stimulates KI in local and surrounding areas. (2) The main mechanisms by which NSC promotes KI include the increase in fiscal investment and talents in science, the improvement of digital infrastructure, as well as the enhancement of urban network centrality and innovation efficiency. (3) Geographical proximity, cooperation proximity, and digitization proximity constitute the main channels of policy spillover. (4) NSC has not shown significant promotion of regional innovation convergence, and its radiation influence needs further improvement. (5) The knowledge innovation effects of NSCs manifest heterogeneity based on the distinct knowledge orientation and innovation environment, with this impact being notably pronounced in application innovation-oriented cities such as Shenzhen. The results of this study reveal the positive yet limited impact of NSC on KI and provide a reference for other economies in the areas of LSRIs, digital infrastructure, and the formulation of place-based innovation policy.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

Quality Measure Adherence and Oral Health Outcomes in Children

Process-based quality measures are generally intended to promote evidence-based practices that have been proven to improve outcomes. However, due to lack of standardized implementation of diagnostic codes in dentistry, assessing the association between process and oral health outcomes has been challenging. To estimate the association of adhering to dental quality measures with patient oral health outcomes. Using a target trial emulation, a causal inference framework, this retrospective cohort study estimated the difference in the risk of developing tooth decay between US children who adhered to process-based dental quality measures (receiving topical fluoride and sealant [treated groups]) and those who did not (control groups). Electronic health records of US children and adolescents aged 0 to 18 years from January 1, 2014, to December 31, 2020, were used. To emulate random treatment assignment based on baseline confounders, coarsened exact matching was used to produce covariate balance between the treated and control groups. A time-to-event regression model produced effect estimates, adjusting for time-varying covariates. Near-far matching was used to account for unmeasured confounders as a sensitivity analysis. Data were analyzed from May 1 to August 7, 2023. Adherence to dental quality measures. Incidence of tooth decay. Among 69 212 US children aged between 0 and 18 years (mean [SD] age, 10.2 [5.0] years; 49.5% male, 50.4% female, and 0.1% unknown or transgender), 1930 (2.8%) were Asian, 2038 (2.9%) were Black, 8667 (12.5%) were Hispanic, 33 632 (48.6%) were White, and 22 945 (33.2%) were multiracial, other, or missing racial and ethnic group identification. Relative to control individuals, treated individuals were more likely to be at elevated risk of caries (fluoride measure: 16 453 [76.5%] vs 15 236 [39.8%]; sealant measure: 2264 [54.6%] vs 997 [44.0%]) and have regular dental visits (fluoride measure: 21 498 [100%] vs 13 741 [35.9%]; sealant measure: 1623 [39.2%] vs 871 [38.4%]). Adherence to quality measures was associated with reduced risk of tooth decay with adjusted hazard ratios of 0.82 (95% CI, 0.78- 0.86) for fluoride and 0.86 (95% CI, 0.76-0.97) for sealant in the matched cohort. Benefits of adhering to quality measures were greater among children at elevated vs low risk and with public vs commercial insurance for both measures. In this cohort study, adhering to dental quality measures was associated with reduced risk of tooth decay, and benefits were greater among children at elevated risk and with public insurance. These findings provide insights in facilitating targeted application of quality measures or developing more tailored quality improvement initiatives.

Principal stratification analysis of noncompliance with time-to-event outcomes.

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

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.

Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift

An adaptive platform trial (APT) is a multi-arm trial in the context of a single disease where treatment arms are allowed to enter or leave the trial based on some decision rule. If a treatment enters the trial later than the control arm, there exist nonconcurrent controls who were not randomized between the two arms under comparison. As APTs typically take long periods of time to conduct, temporal drift may occur, which requires the treatment comparisons to be adjusted for this temporal change. Under the causal inference framework, we propose two approaches for treatment comparisons in APTs that account for temporal drift, both based on propensity score weighting. In particular, to address unmeasured confounders, one approach is doubly robust in the sense that it remains valid so long as either the propensity score model is correctly specified or the time effect model is correctly specified. Simulation study shows that our proposed approaches have desirable operating characteristics with well controlled Type I error rates and high power with or without unmeasured confounders.

Psychosocial stress, adult suicidal ideation, and the mediating effect of poor sleep quality during the COVID-19 pandemic in South Africa.

South Africa instituted one of the world's strictest lockdowns during the COVID-19 pandemic, which generated heightened conditions of psychosocial stress and posed widespread mental health risks. Despite the elevated burdens of suicidal behaviors and risk of psychiatric disease in the country, few studies have examined the impacts of psychosocial stress from the pandemic on suicidal ideation in South Africa. This study examined the association between psychosocial stress experienced under the COVID-19 pandemic and adult suicidal ideation, as well as degree to which sleep quality and duration mediated this relationship. An online survey assessed experiences of COVID-19 psychosocial stress, sleep quality and duration, and suicidal ideation in a sample of 189 South African adults during the second and third waves of the COVID-19 pandemic. A causal inference framework for mediation analysis was used to assess the degree to which sleep quality and duration explained the association between COVID-19 psychosocial stress and suicidal ideation. Suicidal ideation was reported in 21% of adults. Adults described having moderate sleep quality and an average of 6.9 hours of sleep per night. COVID-19 psychosocial stress significantly predicted adult suicidal ideation in fully adjusted models. Sleep quality, but not sleep duration, significantly mediated the association between COVID-19 psychosocial stress and suicidal ideation, accounting for 25.9% of the total effect. Poor sleep quality may play an important role in exacerbating the alarming stress-induced mental health effects of the COVID-19 pandemic. Further research is necessary to understand the underlying sleep dynamics and associated psychological and neurobiological processes that perpetuate adult suicidal ideation.

Probability causal inference of interest rate fluctuations: Evidence from private credit in emerging markets

Preventing risks in the private credit market is an important component of maintaining financial stability, especially in emerging markets. As loan amounts serve as both the cause and the effect of credit interest rates, through the weighted path convergence design of conditional probability and joint probability, this study establishes a probabilistic causal inference framework of loan amounts and interest rates to explore the mechanism of interest rate fluctuations in China's private credit market, market behaviour of the allocation of private loan amount, and the design of policies. The analysis provides several results. (1) The fluctuation of private credit interest rate occurred during the outbreak of the epidemic (2019Q1-2020Q1), the credit interest rate was mainly controlled by the loan amount, and the interest rate fluctuation mainly originated from private credit market behaviour. In the early stage of the post COVID-19 economic recovery (2020Q3-2021Q3), the credit interest rate is mainly controlled by non-loan amount factors, and the interest rate fluctuations occurred mainly because of the non-market behaviour of private credit, which is related to government policy support and the increase in low-interest loans from financial institutions. (2) As a result of market behaviour, for the 19.54 %–22.50 % of high interest rates on borrowing, the probability of high interest rates due to the loan amount increased by 40.48 %. (3) By policy design, reallocating the risky loan amount, the interest rate can be reduced by up to 7.36 % on a highlight-interest rate day. The reallocation of loan amounts for different security methods has varying effects on interest rates reductions, and there may even be ineffective allocations. The findings provide decision support for financial regulators in emerging markets to stabilise interest rate volatility in private credit markets.

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

A New Causal Inference Framework for SAR Target Recognition

A New Causal Inference Framework for SAR Target Recognition

Adaptive hybrid control design for comparative clinical trials with historical control data

Abstract We propose an adaptive hybrid control causal (AHCC) design to leverage historical control data to reduce the sample size demanded by standard randomised controlled trials (RCT). Under the causal inference framework, we define the causal estimand of the average treatment effect and derive the corresponding estimator based on the trial data and historical control data. The AHCC design takes a multistage or group sequential approach. The number of patients randomised to the concurrent control is adaptively adjusted based on the amount of information borrowed from the historical control data. At each stage, based on the interim data, the contribution of the historical control data, quantified by the effective sample size, is updated and used to determine the randomisation ratio between the treatment and control arms for the next stage, with the goal to resemble a standard RCT upon the completion of the trial. Simulation studies show that the AHCC design has desirable operating characteristics. For example, it saves on sample size when substantial information can be borrowed from the historical control, and it maintains power when little information can be borrowed from the historical control.

Educational Outcomes for Children at 7 to 9 Years of Age After Birth at 39 vs 40 to 42 Weeks’ Gestation

Birth at 39 weeks' gestation is common and thought to be safe for mother and neonate. However, findings of long-term outcomes for children born at this gestational age have been conflicting. To evaluate the association of birth at 39 weeks' gestation with childhood numeracy and literacy scores at ages 7 to 9 years compared with birth at 40 to 42 weeks' gestation. In this Australian statewide, population-based cohort study using a causal inference framework based on target trial emulation, perinatal data on births between January 1, 2005, and December 31, 2011, were linked to educational outcomes at 7 to 9 years of age. Statistical analyses were performed from December 2022 to June 2023. Birth at 39 weeks' gestation compared with birth at 40 to 42 weeks' gestation. Numeracy and literacy outcomes were assessed at 7 to 9 years of age using Australian National Assessment Program-Literacy and Numeracy data and defined by overall z score across 5 domains (grammar and punctuation, reading, writing, spelling, and numeracy). Multiple imputation and doubly robust inverse probability weighted regression adjustment were used to estimate population average causal effects. The study population included 155 575 children. Of these children, 49 456 (31.8%; 24 952 boys [50.5%]) were born at 39 weeks' gestation and were compared with 106 119 (68.2%; 52 083 boys [49.1%]) born at 40 to 42 weeks' gestation. Birth at 39 weeks' gestation was not associated with altered educational outcomes for children aged 7 to 9 years compared with their peers born at 40 to 42 weeks' gestation (mean [SE] z score, 0.0008 [0.0019] vs -0.0031 [0.0038]; adjusted risk difference, -0.004 [95% CI, -0.015 to 0.007]). Each educational domain was investigated, and no significant difference was found in grammar and punctuation (risk difference [RD], -0.006 [95% CI, -0.016 to 0.005]), numeracy (RD, -0.009 [95% CI, -0.020 to 0.001]), spelling (RD, 0.001 [95% CI, -0.011 to 0.0013]), reading (RD, -0.008 [95% CI, -0.019 to 0.003]), or writing (RD, 0.006 [95% CI, -0.005 to 0.016]) scores for children born at 39 weeks' gestation compared with those born at 40 to 42 weeks' gestation. Birth at 39 weeks' gestation also did not increase the risk of scoring below national minimum standards in any of the 5 tested domains. Using data from a statewide linkage study to emulate the results of a target randomized clinical trial, this study suggests that there is no evidence of an association of birth at 39 weeks' gestation with numeracy and literacy outcomes for children aged 7 to 9 years.