LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference

TL;DR

This paper introduces the LILI clustering algorithm integrated into causal forests to improve causal inference accuracy by reducing bias and enhancing treatment effect estimation through a novel leaf similarity measure.

Contribution

The paper proposes a new LILI clustering method that connects causal trees based on leaf similarity, improving bias reduction and prediction accuracy in causal forest models.

Findings

LILI clustering reduces bias in causal effect estimation.

The integrated method improves average treatment effect prediction accuracy.

Theoretical analysis confirms convergence of the estimated ATE.

Abstract

Causal forest methods are powerful tools in causal inference. Similar to traditional random forest in machine learning, causal forest independently considers each causal tree. However, this independence consideration increases the likelihood that classification errors in one tree are repeated in others, potentially leading to significant bias in causal e ect estimation. In this paper, we propose a novel approach that establishes connections between causal trees through the Limit Inferior Leaf Interval (LILI) clustering algorithm. LILIs are constructed based on the leaves of all causal trees, emphasizing the similarity of dataset confounders. When two instances with di erent treatments are grouped into the same leaf across a su cient number of causal trees, they are treated as counterfactual outcomes of each other. Through this clustering mechanism, LILI clustering reduces bias present in traditional causal tree methods and enhances the prediction accuracy for the average treatment e ect (ATE). By integrating LILIs into a causal forest, we develop an e cient causal inference method. Moreover, we explore several key properties of LILI by relating it to the concepts of limit inferior and limit superior in the set theory. Theoretical analysis rigorously proves the convergence of the estimated ATE using LILI clustering. Empirically, extensive comparative experiments demonstrate the superior performance of LILI clustering.