Hi-CI: Deep Causal Inference in High Dimensions

TL;DR

Hi-CI introduces a deep learning framework that effectively estimates causal effects in high-dimensional observational data with complex treatments, addressing confounding bias and high-cardinality challenges.

Contribution

The paper presents a novel deep neural network architecture combining decorrelation and outcome prediction networks for high-dimensional causal inference.

Findings

Effective causal effect estimation on synthetic data

Improved accuracy on real-world NEWS dataset

Handles high-dimensional covariates and treatments

Abstract

We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co-variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representation in lower dimensions as compared to the original covariates and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.