arXiv subjects

Falco J. Bargagli Stoffi

Publications and source records attributed to Falco J. Bargagli Stoffi.

4 recordsLinked to original sources

arXiv2026

Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials

Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across pre-specified patient populations. However, these analyses frequently suffer from small sample sizes which limit the power to detect heterogeneous effects. Power can be improved by leveraging predictors of the outcome -- i.e., through covariate adjustment -- as well as by borrowing external data from similar RCTs or observational studies. The benefits of covariate adjustment may be limited when the trial sample is small. Borrowing external data can increase the effective sample size and improve power, but it introduces two key challenges: (i) integrating data across sources can lead to model misspecification, and (ii) practical violations of the positivity assumption -- where the probability of receiving the target treatment is near-zero for some covariate profiles in the external data -- can lead to extreme inverse-probability weights and unstable inferences, ultimately negating potential power gains. To account for these shortcomings, we present an approach to improving power in pre-planned subgroup analyses of small RCTs that leverages both baseline predictors and external data. We propose debiased estimators that accommodate parametric, machine learning, and nonparametric Bayesian methods. To address practical positivity violations, we introduce three estimators: a covariate-balancing approach, an automated debiased machine learning (DML) estimator, and a calibrated DML estimator. We show improved power in various simulations and offer practical recommendations for the application of the proposed methods. Finally, we apply them to evaluate the effectiveness of citalopram for negative symptoms in first-episode schizophrenia patients across subgroups defined by duration of untreated psychosis, using data from two small RCTs.

stat.ME↗

arXiv2025

Estimating the Effects of Heatwaves on Health: A Causal Inference Framework

The harmful relationship between heatwaves and health has been extensively documented in medical and epidemiological literature. However, most evidence is associational and cannot be interpreted causally unless strong assumptions are made. In this paper, we first make explicit the assumptions underlying the statistical methods frequently used in the heatwave literature and demonstrate when these assumptions might break down in heatwave contexts. To address these shortcomings, we propose a causal inference framework that transparently elicits causal identification assumptions. Within this new framework, we first introduce synthetic controls (SC) for estimating heatwave effects, then propose a spatially augmented Bayesian synthetic control (SA-SC) method that accounts for spatial dependence and spillovers. Empirical Monte Carlo simulations show both methods perform well, with SA-SC reducing root mean squared error and improving posterior interval coverage under spillovers and spatial dependence. Finally, we apply the proposed methods to estimate the causal effects of heatwaves on Medicare heat-related hospitalizations among 13,753,273 beneficiaries residing in Northeastern U.S. from 2000 to 2019. This causal inference framework provides spatially coherent counterfactual outcomes and robust, interpretable, and transparent causal estimates while explicitly addressing the unexamined assumptions in existing methods that pervade the heatwave effect literature.

stat.ME↗

arXiv2024

Environmental Justice Implications of Power Plant Emissions Control Policies: Heterogeneous Causal Effect Estimation under Bipartite Network Interference

Emissions generators, such as coal-fired power plants, are key contributors to air pollution and thus environmental policies to reduce their emissions have been proposed. Furthermore, marginalized groups are exposed to disproportionately high levels of this pollution and have heightened susceptibility to its adverse health impacts. As a result, robust evaluations of the heterogeneous impacts of air pollution regulations are key to justifying and designing maximally protective interventions. However, such evaluations are complicated in that much of air pollution regulatory policy intervenes on large emissions generators while resulting impacts are measured in potentially distant populations. Such a scenario can be described as that of bipartite network interference (BNI). To our knowledge, no literature to date has considered estimation of heterogeneous causal effects with BNI. In this paper, we contribute to the literature in a three-fold manner. First, we propose BNI-specific estimators for subgroup-specific causal effects and design an empirical Monte Carlo simulation approach for BNI to evaluate their performance. Second, we demonstrate how these estimators can be combined with subgroup discovery approaches to identify subgroups benefiting most from air pollution policies without a priori specification. Finally, we apply the proposed methods to estimate the effects of coal-fired power plant emissions control interventions on ischemic heart disease (IHD) among 27,312,190 US Medicare beneficiaries. Though we find no statistically significant effect of the interventions in the full population, we do find significant IHD hospitalization decreases in communities with high poverty and smoking rates.

stat.ME↗

arXiv2021

Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy

Despite their popularity, machine learning predictions are sensitive to potential unobserved predictors. This paper proposes a general algorithm that assesses how the omission of an unobserved variable with high explanatory power could affect the predictions of the model. Moreover, the algorithm extends the usage of machine learning from pointwise predictions to inference and sensitivity analysis. In the application, we show how the framework can be applied to data with inherent uncertainty, such as students' scores in a standardized assessment on financial literacy. First, using Bayesian Additive Regression Trees (BART), we predict students' financial literacy scores (FLS) for a subgroup of students with missing FLS. Then, we assess the sensitivity of predictions by comparing the predictions and performance of models with and without a highly explanatory synthetic predictor. We find no significant difference in the predictions and performances of the augmented (i.e., the model with the synthetic predictor) and original model. This evidence sheds a light on the stability of the predictive model used in the application. The proposed methodology can be used, above and beyond our motivating empirical example, in a wide range of machine learning applications in social and health sciences.

econ.EM↗