Probabilistic Matching: Causal Inference under Measurement Errors

TL;DR

This paper introduces a probabilistic approach for causal inference that accounts for measurement errors in key variables, improving accuracy and reducing bias in observational data analysis.

Contribution

The study presents a novel probabilistic method that explicitly models measurement uncertainty to enhance causal inference from noisy observational data.

Findings

Reduces bias in causal estimates with noisy data

Avoids false causal conclusions in most scenarios

Performs well on both simulated and real datasets

Abstract

The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be inferred from other observed attributes. In such cases, inaccuracies of the applied inference methods will result in noisy outputs. In this study, we propose a novel approach for causal inference when one or more key variables are noisy. Our method utilizes the knowledge about the uncertainty of the real values of key variables in order to reduce the bias induced by noisy measurements. We evaluate our approach in comparison with existing methods both on simulated and real scenarios and we demonstrate that our method reduces the bias and avoids false causal inference conclusions in most cases.