Causally Regularized Learning with Agnostic Data Selection Bias

TL;DR

This paper introduces a causally regularized logistic regression method that addresses selection bias in machine learning by identifying stable causal features, improving robustness across different domains without prior knowledge of test data.

Contribution

The paper proposes a novel CRLR algorithm that combines causal feature selection with confounder balancing to enhance model robustness against agnostic data selection bias.

Findings

CRLR outperforms state-of-the-art methods on synthetic and real datasets.

The method effectively identifies causal features with stable effects.

Feature visualization demonstrates interpretability of the model.

Abstract

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.