Learning Deep Features in Instrumental Variable Regression

TL;DR

This paper introduces DFIV, a deep learning-based method for nonlinear instrumental variable regression that learns flexible features to better estimate causal effects from observational data.

Contribution

The paper proposes a novel deep neural network approach for IV regression that handles nonlinear relationships and improves performance over existing methods.

Findings

DFIV outperforms recent state-of-the-art methods on IV benchmarks.

DFIV achieves competitive results in off-policy policy evaluation in reinforcement learning.

The method effectively models complex nonlinear relationships in IV regression.

Abstract

Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by utilizing an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner. DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task.