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Background Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. In a valid PSM design where all baseline confounders are measured and matched, the confounders would be balanced, allowing the treatment status to be considered as if it were randomly assigned. Nevertheless, recent research has unveiled a different facet of PSM, termed “the PSM paradox”. As PSM approaches exact matching by progressively pruning matched sets in order of decreasing propensity score distance, it can paradoxically lead to greater covariate imbalance, heightened model dependence, and increased bias, contrary to its intended purpose. Methods We used analytic formula, simulation, and literature to demonstrate that this paradox stems from the misuse of metrics for assessing chance imbalance and bias. Results Firstly, matched pairs typically exhibit different covariate values despite having identical propensity scores. However, this disparity represents a “chance” difference and will average to zero over a large number of matched pairs. Common distance metrics cannot capture this “chance” nature in covariate imbalance, instead reflecting increasing variability in chance imbalance as units are pruned and the sample size diminishes. Secondly, the largest estimate among numerous fitted models, because of uncertainty among researchers over the correct model, was used to determine statistical bias. This cherry-picking procedure ignores the most significant benefit of matching design-reducing model dependence based on its robustness against model misspecification bias. Conclusions We conclude that the PSM paradox is not a legitimate concern and should not stop researchers from using PSM designs.

This paper presents a simple and efficient estimation of the average treatment effect (ATE) and local average treatment effect (LATE) in models with unmeasured confounders. In contrast to existing studies that estimate some unknown functionals in the influence function either parametrically or semiparametrically, we do not model the influence function nonparametrically. Instead, we apply the calibration method to a growing number of moment restrictions to estimate the weighting functions nonparametrically, and then estimate the ATE and LATE by substitution. The calibration method is similar to the covariate-balancing method in that both methods exploit the moment restrictions. The difference is that the calibration method imposes the sample analogue of the moment restrictions, whereas the covariate-balancing method does not. A simulation study reveals that our estimators have good finite-sample performance and outperform existing alternatives. An application to an empirical analysis of return to education illustrates the practical value of the proposed method.

Propensity score methods have been widely adopted in observational studies, however research on propensity score-based weighting (PSW) methods in complex survey data settings is lacking, particularly for binary outcomes. We conducted a simulation study to compare eight propensity score weighting approaches for estimating treatment effects using survey weighted data. Each of the eight methods is applied to estimation of two measures of the population-level treatment effect: the population average treatment effect (PATE), and the population average treatment effect on the treated (PATT). The methods are compared in terms of mean relative bias and coverage probability under different scenarios by varying the treatment effect, degrees of model misspecification, and levels of overlap in the propensity score. The results demonstrate that the two-stage methods with predicted outcomes weighted by survey weights consistently outperform the other methods for estimating the PATT; for estimating the PATE, the best performing PSW method depends on the degree of model misspecification and propensity score overlap. When the outcome model is correctly specified, four two-stage methods produce better estimates depending on the propensity score overlap. The methods are applied to the 2015 National Health Interview Survey data to estimate the effect of provider-patient discussion about smoking on smoking cessation.

Background Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation. Methods To obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention. Results The estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was −0.019 (95% CI: −0.040, −0.007) and that among those who have less than a high school education in India (ATU) was −0.012 (95% CI: −0.036, 0.010). Conclusions The g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.

Background An infant’s birth weight, typically measured within the first hours after birth, is crucial for assessing their health. Low birth weight (LBW) can result from intrauterine growth restriction, preterm birth, or a combination of both factors. This study employs Bayesian propensity score (BPS) methods to estimate the causal effect of midwife-led continuity care (MLCC) on LBW in Ethiopia’s North Shoa Zone, Amhara Regional State. Using quasi-experimental data, we address covariate imbalance between MLCC and other professional groups, with simulations validating the robustness of our findings. Methods A prospective non-randomized (quasi-experimental study design) was employed from August 2019 to September 2020 in the North Shoa Zone, Amhara Regional State, Ethiopia. Markov Chain Monte Carlo algorithms were employed in Bayesian causal inference approaches to estimate the average treatment effect. A simulation study was conducted to evaluate the performance of the standard nearest-neighbor Propensity score and the weighting BPS methods for estimating the average treatment effect (ATE) on LBW. Results Our analysis showed that the MLCC reduced the risk of LBW by 24% (ATE: −2.39, 95% CI: −12.4 to 7.63). Bayesian methods estimated ATE more precisely (2.065, SE = 0.875) than Frequentist ones (2.156, SE = 0.86), due to better noise handling and use of more data. Although bias differences weren’t significant, Bayesian estimates proved more reliable. Key LBW predictors include uterine height, emergency cesarean, and nutrition. These findings support MLCC as a valuable strategy for improving neonatal outcomes in low-resource settings. Conclusion The counterfactual estimate of the Bayesian method indicates that the Bayesian credible interval effect of the LBW of newborn babies reliably estimates the true causal relationship. The sensitivity analysis demonstrated the robustness of our findings, with minimal variation in adjusted odds ratio and ATE across alternative priors and matching methods, and showed the reliability of the BPS approach in producing consistent and credible causal inferences. More attention should be given to pregnant women with an abortion history, with high blood pressure, with more folic acid, with an emergency cesarean, women with premature babies, and women with low nutritional status.

Instrumental variable (IV) analysis addresses bias owing to unmeasured confounding when comparing two nonrandomized treatment groups. To date, studies in the statistical and biomedical literature have focused on the local average treatment effect (LATE), the average treatment effect for compliers. In this article, we study the weighted local average treatment effect (WLATE), which represents the weighted average treatment effect for compliers. In the WLATE, the population of interest is determined by either the instrumental propensity score or compliance score, or both. The LATE is a special case of the proposed WLATE, where the target population is the entire population of compliers. Here, we discuss the interpretation of a few special cases of the WLATE, identification results, inference methods, and optimal weights. We demonstrate the proposed methods with two published examples in which considerations of local causal estimands that deviate from the LATE are beneficial.

The Vietnamese Government have been implementing the Payment for Forest Ecosystem Service (PFES) since 2008 with the aim of both improving natural forest status and enhancing income for mountainous community. Yet, effectiveness of the PFES scheme is now debated because of the shortage of experimental studies. So, the overall purpose of this study is to measure the effectiveness of the PFES program by propensity scores analysis. To do so, the study randomly surveyed 469 households located in four districts across Quang NAM province and then estimated the Average Treatment Effect on Treated (ATET). It is found that: (1) the households within PFES had got a insignificantly higher income than those without PES in the short-run; (2) yet, PFES was effective in long-run due to the improvement on income for participants; (3) PFES had an important role in increasing income inequality. Although this study demonstrated reasonable results, some limitations still exist due to the objective reasons, thus more studies with alternative methods should be conducted to confirm the results of this study for better policies.

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

We propose a method for using instrumental variables (IV) to draw inference about causal effects for individuals other than those affected by the instrument at hand. Policy relevance and external validity turn on the ability to do this reliably. Our method exploits the insight that both the IV estimand and many treatment parameters can be expressed as weighted averages of the same underlying marginal treatment effects. Since the weights are identified, knowledge of the IV estimand generally places some restrictions on the unknown marginal treatment effects, and hence on the values of the treatment parameters of interest. We show how to extract information about the treatment parameter of interest from the IV estimand and, more generally, from a class of IV-like estimands that includes the two stage least squares and ordinary least squares estimands, among others. Our method has several applications. First, it can be used to construct nonparametric bounds on the average causal effect of a hypothetical policy change. Second, our method allows the researcher to flexibly incorporate shape restrictions and parametric assumptions, thereby enabling extrapolation of the average effects for compilers to the average effects for different or larger populations. Third, our method can be used to test model specification and hypotheses about behavior, such as no selection bias and/or no selection on gain.

The Vietnamese Government have been implementing the Payment for Forest Ecosystem Service (PFES) since 2008 with the aim of both improving natural forest status and enhancing income for mountainous community. Yet, effectiveness of the PFES scheme is now debated because of the shortage of experimental studies. So, the overall purpose of this study is to measure the effectiveness of the PFES program by propensity scores analysis. To do so, the study randomly surveyed 469 households located in four districts across Quang Nam province and then estimated the Average Treatment Effect on Treated (ATET). It is found that: (1) the households within PFES had got a insignificantly higher income than those without PES in the short-run; (2) yet, PFES was effective in long-run due to the improvement on income for participants; (3) PFES had an important role in increasing income inequality. Although this study demonstrated reasonable results, some limitations still exist due to the objective reasons, thus more studies with alternative methods should be conducted to confirm the results of this study for better policies.