Continual Causal Inference with Incremental Observational Data

no code implementations • 3 Mar 2023 • Zhixuan Chu, Ruopeng Li, Stephen Rathbun, Sheng Li

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.

Causal Inference counterfactual +3

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data

no code implementations • 22 Feb 2022 • Zhixuan Chu, Stephen Rathbun, Sheng Li

In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.

Causal Inference Representation Learning +1

Continual Lifelong Causal Effect Inference with Real World Evidence

no code implementations • 1 Jan 2021 • Zhixuan Chu, Stephen Rathbun, Sheng Li

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.

counterfactual Representation Learning +1