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New PPCI Framework Enhances Causal Inference with Auxiliary Data

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a new framework called Prediction-Powered Causal Inference (PPCI) to improve the estimation of causal and structural parameters. This method leverages unlabeled auxiliary regressors alongside labeled data to achieve smaller asymptotic variances than methods using only labeled observations. The proposed DML-PPCI methods, including EE-DML-PPCI and TMLE-DML-PPCI, are designed to match a derived efficiency bound and utilize Neyman orthogonal scores for estimation. AI

RANK_REASON The cluster contains an academic paper detailing a new statistical method.

Read on arXiv stat.ML →

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New PPCI Framework Enhances Causal Inference with Auxiliary Data

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Masahiro Kato ·

    Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression

    arXiv:2606.12892v1 Announce Type: new Abstract: This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consistin…

  2. arXiv stat.ML TIER_1 English(EN) · Masahiro Kato ·

    Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression

    This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consisting of outcomes and regressors. Our goal is to con…

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