Probabilistic Matching: Causal Inference under Measurement Errors
This addresses the challenge of accurate causal inference for researchers and practitioners dealing with noisy observational data, but it is incremental as it builds on existing methods.
The paper tackles the problem of causal inference from observational data when key variables are noisy, proposing a method that uses uncertainty knowledge to reduce bias and showing it reduces bias and avoids false conclusions in most cases.
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.