Potential Outcomes Framework Research Articles

Sexual Harassment, Sexual Violence, and Mental Health Outcomes: Causal Inference with Ambiguous Exposures.

Social exposures and their impact on mental health has proven hard to capture, partly owing to the complex and multifaceted nature of social reality. Sexual harassment and sexual violence (SHV) are no exceptions. SHV can be conceptualized as a continuum of negative sexual experiences whose severity vary depending on multiple determinants. Further, SHV can be conceptualized as either discrete events or as a generally hostile sexual environment represented by latent variables. With any of these conceptualizations, SHV constitutes a broad construct containing many kinds of negative experiences. This ambiguity poses challenges for determining the mental health consequences, as different forms of SHV may vary in terms of their mental health impact. We discuss different conceptualizations of SHV in relation to mental health outcomes through the lens of the potential outcomes framework, with a focus on the consistency condition. The multiple versions of treatment theory is presented to show how to provide formal interpretations of causal estimates under ambiguous exposures. Lastly, we provide suggestions on how the increase the clarity and interpretability of the effects of SHV on mental health, by increasing the precision of the causal questions and the use of more specific definitions of SHV.

Did Religious Well-Being Benefits Converge or Diverge During the Early Stages of the COVID-19 Pandemic in Germany?

A large body of literature highlights the benefits of being religious in terms of subjective well-being. We examine changes to these so-called religious well-being benefits during the early stages of the COVID-19 pandemic in Germany and address the role of (formal and informal) social integration when explaining these changes. We empirically test two contrasting scenarios: The first scenario predicts a decrease in religious well-being benefits (convergence hypothesis), while the second scenario predicts an increase in religious well-being benefits (divergence hypothesis). We adopt a potential outcomes framework and apply marginal structural models and inverse probability of treatment weighting to nationally representative, longitudinal data including both pre- and during-pandemic periods. Thereby, we show that initial religious well-being benefits declined during the early stages of the COVID-19 pandemic in Germany. This decline was partly due to religious individuals’ perception of decreasing social integration. Our results challenge the widespread idea that religious individuals are better protected against crises.

Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments

AbstractStepped wedge cluster randomized experiments (SW‐CREs) represent a class of unidirectional crossover designs. Although SW‐CREs have become popular, definitions of estimands and robust methods to target estimands under the potential outcomes framework remain insufficient. To address this gap, we describe a class of estimands that explicitly acknowledge the multilevel data structure in SW‐CREs and highlight three typical members of the estimand class that are interpretable. We then introduce four analysis of covariance (ANCOVA) working models to achieve estimand‐aligned analyses with covariate adjustment. Each ANCOVA estimator is model‐assisted, as its point estimator is consistent even when the working model is misspecified. Under the stepped wedge randomization scheme, we establish the finite population Central Limit Theorem for each estimator. We study the finite‐sample operating characteristics of the ANCOVA estimators in simulations and illustrate their application by analyzing the Washington State Expedited Partner Therapy study.

An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.

In 1965, Sir Austin Bradford Hill articulated nine viewpoints for evaluating whether a body of evidence about the relationship between an exposure and outcome should be interpreted causally. In this commentary, we highlight a selection of the ways in which these viewpoints have had an impact on the field of epidemiology in terms of methods development, study design, and interpretation of results. Additionally, we opine on how the viewpoints relate to our understanding of basic epidemiologic concepts - for example, our choice of absolute or relative measures of effect, our evolving understanding of the role of context in the generalizability of study results, and modern epistemologies for causal inference (i.e., the potential outcomes framework and graphical causal models). Hill cautioned his audience that evidence should be weighed according to the policy choice it would inform and the context in which that policy would be implemented. We root our remarks in considerations of the public health impact of our conclusions about the causal nature of an observed relationship.

From urn models to box models: Making Neyman's (1923) insights accessible

Abstract Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé; Hodges and Lehmann; Freedman, Pisani, and Purves; and Dunning). These examples illustrate that Neyman’s key insights can be explained in intuitive and interesting ways to audiences at all levels, including undergraduates in introductory statistics courses. We have found Freedman, Pisani, and Purves’s box-of-tickets model to be a valuable expository tool, and we also find their intuitive explanation of Neyman’s variance result helpful: It is a “minor miracle” that in randomized experiments, the two-sample z z -test is conservative because of “two mistakes that cancel.” All four books take a more positive view of Neyman’s results than Neyman himself did. We encourage educators and researchers to explore ways to communicate Neyman’s ideas that are helpful for their own audiences.

A Primer on Deep Learning for Causal Inference

This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch.

Evaluating individualized treatment effect predictions: A model-based perspective on discrimination and calibration assessment.

In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined based on the potential outcomes framework, which facilitates a comparison of existing and novel measures. In particular, we examine existing measures of discrimination for benefit (variations of the c-for-benefit), and propose model-based extensions to the treatment effect setting for discrimination and calibration metrics that have a strong basis in outcome risk prediction. The main focus is on randomized trial data with binary endpoints and on models that provide individualized treatment effect predictions and potential outcome predictions. We use simulated data to provide insight into the characteristics of the examined discrimination and calibration statistics under consideration, and further illustrate all methods in a trial of acute ischemic stroke treatment. The results show that the proposed model-based statistics had the best characteristics in terms of bias and accuracy. While resampling methods adjusted for the optimism of performance estimates in the development data, they had a high variance across replications that limited their accuracy. Therefore, individualized treatment effect models are best validated in independent data. To aid implementation, a software implementation of the proposed methods was made available in R.

Introducing DigiCAT: A digital tool to promote the principled use of counterfactual analysis for identifying potential active ingredients in mental health

Background Given the challenges and resources involved in mental health intervention development and evaluation, it is valuable to obtain early evidence on which intervention targets represent the most promising investments. Observational datasets provide a rich resource for exploring these types of questions; however, the lack of randomisation to treatments in these data means they are vulnerable to confounding issues. Counterfactual analysis refers to a family of techniques within the potential outcomes framework that can help address confounding. In doing so, they can help differentiate potential intervention targets that may reflect genuine active ingredients in mental health from those that are only associated with mental health outcomes due to their common dependence on ‘third variables’. However, counterfactual analysis is rarely used for this purpose and where it is used in health research it is often implemented in a suboptimal fashion. One key reason may be a lack of accessible tutorials and software that embeds best practices. Methods To help promote the principled use of counterfactual analysis we developed DigiCAT. DigiCAT is an open digital tool built in R and Shiny that implements a range of counterfactual analysis methods. It is accompanied by accessible tutorials. The tool has been designed to handle real data, with capabilities for missing data, non-binary treatment effects, and complex survey designs. Results The current article describes the development of DigiCAT, drawing on user and lived experience expert input and provides an overview of its features and examples of its uses. Conclusions Counterfactual analysis could help prioritise intervention targets by establishing which ones remain associated with mental health outcomes after accounting for potential confounding. Accessible digital tools supported by clear guidance may help promote the uptake and principled use of these techniques.

Semiparametric efficiency gains from parametric restrictions on propensity scores

Abstract We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.

Funding the Artistic Future: Diversifying Support for Sustainable Artistic Impact Through Program Evaluation

This paper presents a strategic impact and assessment approach—the Rubin-Imbens potential outcomes framework—to help identify the causal effects of initiatives for arts and culture organizations, which is increasingly required by the public and stakeholders in an era of financial precarity and digitization. This framework not only strengthens the argument for diversified arts funding, but also equips organizations with the tools necessary for developing sustainable business models. After embedding the proposal in the context of prior literature, I provide some practical guidance that organizations can use to achieve operational sustainability without compromising their artistic missions. The adoption of the Rubin-Imbens framework in arts management has the potential to produce more rigorous impact assessments that ultimately lead to improved outcomes and funding opportunities.

When causality meets fairness: A survey

Addressing the problem of fairness is crucial to safely using machine learning algorithms to support decisions that have a critical impact on people's lives, such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fairness have been defined and examined in the past decade, such as statistical parity and equalized odds. However, the most recent notions of fairness are causal-based and reflect the now widely accepted idea that using causality is necessary to appropriately address the problem of fairness. This paper examines an exhaustive list of causal-based fairness notions and studies their applicability in real-world scenarios. As most causal-based fairness notions are defined in terms of non-observable quantities (e.g., interventions and counterfactuals), their deployment in practice requires computing or estimating those quantities using observational data. This paper offers a comprehensive report of the different approaches to infer causal quantities from observational data, including identifiability (Pearl's SCM framework) and estimation (potential outcome framework). The main contributions of this survey paper are (1) a guideline to help select a suitable causal fairness notion given a specific real-world scenario and (2) a ranking of the fairness notions according to Pearl's causation ladder, indicating how difficult it is to deploy each notion in practice.

Causal inference for treatment effects in partially nested designs.

artially nested designs (PNDs) are common in intervention studies in psychology and other social sciences. With this design, participants are assigned to treatment and control groups on an individual basis, but clustering occurs in some but not all groups (e.g., the treatment group). In recent years, there has been substantial development of methods for analyzing data from PNDs. However, little research has been done on causal inference for PNDs, especially for PNDs with nonrandomized treatment assignments. To reduce the research gap, in the current study, we used the expanded potential outcomes framework to define and identify the average causal treatment effects in PNDs. Based on the identification results, we formulated the outcome models that could produce treatment effect estimates with causal interpretation and evaluated how alternative model specifications affect the causal interpretation. We also developed an inverse propensity weighted (IPW) estimation approach and proposed a sandwich-type standard error estimator for the IPW-based estimate. Our simulation studies demonstrated that both the outcome modeling and the IPW methods specified following the identification results can yield satisfactory estimates and inferences of the average causal treatment effects. We applied the proposed approaches to data from a real-life pilot study of the Pregnant Moms' Empowerment Program for illustration. The current study provides guidance and insights on causal inference for PNDs and adds to researchers' toolbox of treatment effect estimation with PNDs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain

The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function μ(t) (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values—that is, estimating μ(t) for very large t. Our approach is grounded in the field of statistics known as extreme value theory. We outline key assumptions for the identifiability of the extreme treatment effect. Additionally, we present a novel and consistent estimation procedure that can potentially reduce the dimension of the confounders to at most 3. This is a significant result since typically, the estimation of μ(t) is very challenging due to high-dimensional confounders. In practical applications, our framework proves valuable when assessing the effects of scenarios such as drug overdoses, extreme river discharges, or extremely high temperatures on a variable of interest.

Average User-Side Counterfactual Fairness for Collaborative Filtering

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub-user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea is to achieve model fairness from a causal perspective. The remaining challenge is that we have no access to interventions, i.e., the counterfactual world that produces recommendations when each user has changed the sensitive attribute value. To this end, we first borrow the Rubin-Neyman potential outcome framework to define average causal effects of sensitive attributes. Next, we show that removing causal effects of sensitive attributes is equal to average counterfactual fairness in CF. Then, we use the propensity re-weighting paradigm to estimate the average causal effects of sensitive attributes and formulate the estimated causal effects as an additional regularization term. To the best of our knowledge, we are one of the first few attempts to achieve counterfactual fairness from the causal effect estimation perspective in CF, which frees us from building sophisticated causal graphs. Finally, experiments on three real-world datasets show the superiority of our proposed model.

Causal mediation analysis with a three-dimensional image mediator.

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies and so forth. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that cesarean section may have a negative effect on intellectual performance and that this effect is mediated by white matter development. Additional prospective and longitudinal studies may be necessary to validate these emerging findings.

Defining, identifying, and estimating causal effects with the potential outcomes framework: a review for education research

Defining, identifying, and estimating causal effects with the potential outcomes framework: a review for education research

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands. Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

Heterogeneous Treatment Effect-based Random Forest: HTERF

Estimates of causal effects are needed to answer what-if questions about shifts in policy, such as new treatments in pharmacology or new pricing strategies for business owners. A new non-parametric approach is proposed to estimate the heterogeneous treatment effect based on random forests (HTERF). The potential outcome framework with unconfoundedness shows that the HTERF is pointwise almost surely consistent with the true treatment effect. Interpretability results are also presented.

SeaTE: Subjective ex ante Treatment Effect of Health on Retirement

The paper studies the effect of health on work among older workers by eliciting two- and four-year-ahead subjective probabilities of working under alternative health states. These measures predict work outcomes. Person-specific differences in working probabilities across health states can be interpreted as Subjective ex ante Treatment Effects (SeaTEs) in a potential outcomes framework and as taste for work within a discrete choice dynamic programming framework. There is substantial heterogeneity in expectations of work conditional on health. The paper shows how heterogeneity in taste for work correlated with health can bias regression estimates of the effect of health on retirement. (JEL D84, I12, J14, J22, J26)

Using causal mediation to examine self-efficacy as a mechanism through which continuing care interventions reduce alcohol use.

Understanding the causal mechanisms through which telephone and mobile health continuing care approaches reduce alcohol use can help develop more efficient interventions that effectively target these mechanisms. Self-efficacy for successfully coping with high-risk alcohol relapse situations is a theoretically and empirically supported mediator of alcohol treatment. This secondary analysis aims to examine self-efficacy as a mechanism through which remote-delivered continuing care interventions reduce alcohol use. The study included 262 adults (Mage = 46.9, SD = 7.4) who had completed 3 weeks of an intensive outpatient alcohol treatment program. The sample was predominantly male (71%), African American (82%), and completed a high school education (71%). The four-arm randomized clinical trial compared three active continuing care interventions (telephone monitoring and counseling [TMC], addiction comprehensive health enhancement support system [ACHESS], and combined delivery of TMC and ACHESS) to usual care and assessed longitudinal measures of alcohol use and self-efficacy. Analyses employed the potential outcomes framework and sensitivity analyses to address threats to causal inference resulting from an observed mediator variable. Relative to usual care, the two intervention conditions that included TMC reduced alcohol use through improvements to self-efficacy. There was no evidence that self-efficacy mediated the effect of ACHESS on alcohol use. Based on our findings, self-efficacy is an important mechanism through which telephone continuing care interventions affect alcohol use. Future research to identify which components of TMC influence self-efficacy and factors that mediate ACHESS effects could enhance the effectiveness of remote delivery of continuing care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).