Deep Learning for Causal Inference

TL;DR

This paper introduces deep learning methods for causal inference, including autoencoder-based neighbor matching and a neural network approach called PropensityNet, demonstrating improved accuracy over traditional techniques.

Contribution

It presents novel deep learning techniques for causal inference, enhancing neighbor and propensity score matching with autoencoders and neural networks.

Findings

Autoencoder-based neighbor matching outperforms k-NN and manifold learning.

PropensityNet surpasses logistic regression in propensity score estimation.

Deep learning methods improve causal effect estimation accuracy.

Abstract

In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the use of autoencoders for dimensionality reduction while maintaining the local neighborhood structure among the data points in the embedding space. This deep learning based technique is shown to perform better than simple k nearest neighbor matching for estimating treatment effects, especially when the data points have several features/covariates but reside in a low dimensional manifold in high dimensional space. We also observe better performance than manifold learning methods for neighbor matching. 2. Propensity score matching is one specific and popular way to perform matching in order to estimate average and individual treatment effects. We propose the use of deep neural networks (DNNs) for propensity score matching, and present a network called PropensityNet for this. This is a generalization of the logistic regression technique traditionally used to estimate propensity scores and we show empirically that DNNs perform better than logistic regression at propensity score matching. Code for both methods will be made available shortly on Github at: https://github.com/vikas84bf