  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
