Abstract

Instrumental variable methods are very popular in econometrics and biostatistics for inferring causal average effects of an exposure on an outcome where there is unmeasured confounding. However, their application for learning heterogeneous treatment effects, such as conditional average treatment effects (CATE), in combination with machine learning, is somewhat limited.

A generic approach that allows the use of arbitrary machine learning algorithms can be based on the popular two-stage principle. We first “regress” the exposure on the instrumental variables (and pre-exposure covariates) and then learn the causal treatment effects by regressing the outcome on the predicted exposure. This is the approach of Foster and Syrgkanis (2023), referred to as IV-debiased machine learning (IV-DML). Unfortunately, the slow convergence rates of the data-adaptive estimators that affect the first-stage predictions propagate into the resulting CATE estimates.

In view of this, we propose the IV-learner, inspired by infinite-dimensional targeted learning procedures (Vansteelandt 2023, van der Laan et al 2024). It strategically targets the first-stage predictions so they perform well in their ultimate task: CATE estimation. The resulting learner is easy to construct based on arbitrary, off-the-shelf algorithms.

We study the finite sample performance of our proposal using simulations, and compare it to existing methods. We also illustrate it using a real data example.