Causal Decision Trees

Abstract

Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree where nodes have causal interpretations. Our method follows a well established causal inference framework and makes use of a classic statistical test. The method is practical for finding causal signals in large data sets.

References in corpus (4)

• Matching Methods for Causal Inference: A Review and a Look Forward
• Recursive Partitioning for Heterogeneous Causal Effects
• Context-Specific Independence in Bayesian Networks
• Doubly Robust Policy Evaluation and Learning

Cited by in corpus (5)

• Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
• Discovering Markov Blanket from Multiple interventional Datasets
• The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions
• Discovering Context Specific Causal Relationships
• Optimal Causal Imputation for Control