arXiv subjects

Johan de Aguas

Publications and source records attributed to Johan de Aguas.

4 recordsLinked to original sources

arXiv2026

Interpolated stochastic interventions based on propensity scores, target policies and treatment-specific costs

We introduce two families of stochastic interventions with discrete treatments that connect causal modeling to cost-sensitive decision making. The interventions arise from a cost-penalized information projection of the independent product of the organic propensity scores and a reference policy, yielding closed-form Boltzmann-Gibbs couplings. The induced marginals define modified stochastic policies that interpolate smoothly, via a tilt parameter, from the organic law or from the reference law toward a product-of-experts limit when all destination costs are strictly positive. The first family recovers and extends incremental propensity score interventions, retaining identification without global positivity. For inference on the expected outcomes after these policies, we derive the efficient influence functions under a nonparametric model and construct one-step estimators. In simulations, the proposed estimators improve stability and robustness to nuisance misspecification relative to plug-in baselines. The framework can operationalize graded scientific hypotheses under realistic constraints. Because inputs are modular, analysts can sweep feasible policy spaces, prototype candidates, and align interventions with budgets and logistics before committing experimental resources.

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arXiv2025

Causal inference amid missingness-specific independencies and mechanism shifts

The recovery of causal effects in structural models with missing data often relies on $m$-graphs, which assume that missingness mechanisms do not directly influence substantive variables. Yet, in many real-world settings, missing data can alter decision-making processes, as the absence of key information may affect downstream actions and states. To overcome this limitation, we introduce $lm$-SCMs and $lm$-graphs, which extend $m$-graphs by integrating a label set that represents relevant context-specific independencies (CSI), accounting for mechanism shifts induced by missingness. We define two causal effects within these systems: the Full Average Treatment Effect (FATE), which reflects the effect in a hypothetical scenario had no data been missing, and the Natural Average Treatment Effect (NATE), which captures the effect under the unaltered CSIs in the system. We propose recovery criteria for these queries and present doubly-robust estimators for a graphical model inspired by a real-world application. Simulations highlight key differences between these estimands and estimation methods. Findings from the application case suggest a small effect of ADHD treatment upon test achievement among Norwegian children, with a slight effect shift due to missing pre-tests scores.

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arXiv2025

Recovery and inference of causal effects with sequential adjustment for confounding and attrition

Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjustment Criteria (SAC), which extend available graphical conditions for recovering causal effects from confounding and attrition using sequential regressions, allowing for the inclusion of post-exposure and forbidden variables in the adjustment sets. We propose an estimator for the recovered Average Treatment Effect (ATE) based on Targeted Minimum-Loss Estimation (TMLE), which exhibits multiple robustness under certain technical conditions. This approach ensures consistency even in scenarios where the Double Inverse Probability Weighting (DIPW) and the naive plug-in sequential regressions approaches fall short. Through a simulation study, we assess the performance of the proposed estimator against alternative methods across different graph setups and model specification scenarios. As a motivating application, we examine the effect of pharmacological treatment for Attention-Deficit/Hyperactivity Disorder (ADHD) upon the scores obtained by diagnosed Norwegian schoolchildren in national tests using observational data ($n=9\,352$). Our findings align with the accumulated clinical evidence, affirming a positive but small impact of medication on academic achievement.

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arXiv2025

The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference

We define the Probability of Tiered Benefit in scenarios with a binary exposure and an outcome that is either categorical with $K \geq 2$ ordered tiers or continuous partitioned by $K-1$ fixed thresholds into disjoint intervals. Similarly to other pure counterfactual queries, this parameter is not $g$-identifiable without additional assumptions. We demonstrate that strong monotonicity does not suffice for point identification when $K \geq 3$ and provide sharp bounds both with and without such constraint. Inference and uncertainty quantification for these bounds are challenging tasks due to potential nonregularity induced by ambiguities in the underlying individualized optimization problems. Such ambiguities can arise from immunities or null treatment effects in subpopulations with positive probability, affecting the lower bound estimate and hindering conservative inference. To address these issues, we extend the available Stabilized One-Step Correction (S1S) procedure by incorporating stratum-specific stabilizing matrices. Through simulations, we illustrate the benefits of this approach over existing alternatives. We apply our method to estimate bounds on the probabilities of tiered benefit and harm from pharmacological treatment for ADHD upon academic achievement, employing observational data from diagnosed Norwegian schoolchildren. Our findings indicate that while girls and children with low prior test performance could have moderate chances of both benefit and harm from treatment, a clear-cut recommendation remains uncertain across all strata.

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