CONTINUAL LIFELONG CAUSAL EFFECT INFERENCE WITH REAL WORLD EVIDENCE Anonymous authors Paper under double-blind review

Abstract

The era of real world evidence has witnessed an increasing availability of observational data, which much facilitates the development of causal effect inference. Although significant advances have been made to overcome the challenges in causal effect estimation, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data. In this paper, we investigate a new research problem of causal effect inference from incrementally available observational data, and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining the selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect inference and the effectiveness of our method.

1. INTRODUCTION

Causal effect inference is a critical research topic across many domains, such as statistics, computer science, public policy, and economics. Randomized controlled trials (RCT) are usually considered as the gold-standard for causal effect inference, which randomly assigns participants into a treatment or control group. As the RCT is conducted, the only expected difference between the treatment and control groups is the outcome variable being studied. However, in reality, randomized controlled trials are always time-consuming and expensive, and thus the study cannot involve many subjects, which may be not representative of the real-world population the intervention would eventually target. Nowadays, estimating causal effects from observational data has become an appealing research direction owing to a large amount of available data and low budget requirements, compared with RCT (Yao et al., 2020) . Researchers have developed various strategies for causal effect inference with observational data, such as tree-based methods (Chipman et al., 2010; Wager & Athey, 2018) , representation learning methods (Johansson et al., 2016; Li & Fu, 2017; Shalit et al., 2017; Chu et al., 2020) , adapting Bayesian algorithms (Alaa & van der Schaar, 2017), generative adversarial nets (Yoon et al., 2018 ), variational autoencoders (Louizos et al., 2017) and so on. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods only focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from the only one source. This assumption is unsubstantial in practice due to two reasons. The first one is based on the characteristics of observational data, which are incrementally available from non-stationary data distributions. For instance, the number of electronic medical records in one hospital is growing every day, or the electronic medical records for one disease may be from different hospitals or even different countries. This characteristic implies that one cannot have access to all observational data at one time point and from one single source. The second reason is based on the realistic consideration of accessibility. For example, when the new observational are available, if we want to refine the model previously trained by original data, maybe 1

