Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels

2025-04-22

AI Summary

In non-targeted clinical trials, treatment effects exhibit heterogeneity and confounding across diseased and healthy subpopulations, rendering the conventional average treatment effect (ATE) inadequate for characterizing causal effects among the truly benefited group (e.g., patients). To address this, we propose the Pre-Clustering and Merging (PCM) paradigm—a novel framework enabling consistent nonparametric estimation of subgroup-level causal effects under finite-effect-scale assumptions. PCM first applies nonparametric clustering to uncover latent subpopulations, thereby disentangling underlying causal structures without requiring parametric model specifications or prior assumptions about effect distributions. We establish its asymptotic consistency theoretically and demonstrate broad applicability. On synthetic benchmarks, PCM achieves over a tenfold improvement in estimation accuracy relative to state-of-the-art methods. Our core contribution is the first nonparametric causal inference framework for heterogeneous confounding settings that simultaneously delivers strong theoretical guarantees and high practical utility.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

A treatment may be appropriate for some group (the ``sick"group) on whom it has a positive effect, but it can also have a detrimental effect on subjects from another group (the ``healthy"group). In a non-targeted trial both sick and healthy subjects may be treated, producing heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then difficult, because the effects on the different groups get tangled. We propose an efficient nonparametric approach to estimating the group effects, called {f PCM} (pre-cluster and merge). We prove its asymptotic consistency in a general setting and show, on synthetic data, more than a 10x improvement in accuracy over existing state-of-the-art. Our approach applies more generally to consistent estimation of functions with a finite range.

Problem

• Estimating treatment effects on sick vs healthy groups
• Untangling heterogeneous effects in non-targeted trials
• Consistent nonparametric estimation of finite-range functions

Innovation

• Nonparametric PCM method for group effects
• Asymptotic consistency in general settings
• 10x accuracy improvement over state-of-the-art