Continual Causal Inference with Incremental Observational Data

TL;DR

This paper introduces a continual causal inference method that estimates causal effects from incrementally available, non-stationary observational data, addressing industrial challenges with limited data storage and distribution shifts.

Contribution

It proposes a novel continual causal effect representation learning approach that handles non-stationary data and limited memory, advancing industrial causal inference applications.

Findings

Effective in non-stationary environments

Outperforms existing methods in causal effect estimation

Maintains estimation accuracy over time

Abstract

The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new industrial problem of causal effect estimation 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 effects 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 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 estimation and the effectiveness of our method.