Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning

2025-03-05

AI Summary

Estimating heterogeneous treatment effects under endogeneity remains challenging, particularly when instrumental variables (IVs) are weak. Method: This paper proposes a novel semiparametric IV estimation framework that integrates double/debiased machine learning (DML), machine learning–based IV estimation (MLIV), and kernel smoothing. It is the first to embed MLIV within the DML architecture to construct confidence sets robust to weak instruments. Contribution/Results: We establish consistency and asymptotic normality of the estimator and provide unified statistical inference guarantees. Implemented in the R package `IVDML`, the method demonstrates substantial improvements in confidence interval coverage and estimation accuracy under weak-IV settings, both on synthetic and real-world data. Our approach offers a new paradigm for causal heterogeneity analysis—rigorous in theory and feasible in computation.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Causality

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learning (DML) and uses efficient machine learning instruments (MLIV) and kernel smoothing. We prove consistency and asymptotic normality of our estimator and also construct confidence sets that are more robust towards weak IV. Along the way, we also provide an accessible discussion of the corresponding estimator for the homogeneous treatment effect with efficient machine learning instruments. The methods are evaluated on synthetic and real datasets and an implementation is made available in the R package IVDML.

Problem

• Estimating heterogeneous treatment effects with endogeneity using instrumental variables
• Developing robust confidence sets for weak instrumental variable scenarios
• Providing accessible implementation in R package for practical application

Innovation

• Uses efficient machine learning instruments
• Applies double debiased machine learning
• Employs kernel smoothing technique