Econometric Inference with Machine-Learned Proxies: Partial Identification via Data Combination

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   Locally Robust Inference with Outcomes Predicted by Machine Learning

   J. Escanciano, Alejandro Puerta-Cuartas · 2026 · Not summarized yet

   Abstract

   Many parameters of interest in economics depend on outcomes generated by machine learning (ML).These generated outcomes are subsequently used in standard econometric estimation and typically treated as observed, a practice that threatens the validity of downstream inference. Empirically relevant cases include dynamic treatment effects under sequential exogeneity, long-term effects with surrogates, the partially linear model with an ML-predicted outcome, productivity persistence with an unrestricted production function and constructed instruments, and outcomes residualized of confounding variation. This paper develops a three-step locally robust GMM framework for valid inference when the depe…

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   Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

   Vasilis Syrgkanis, Victor Lei, Miruna Oprescu et al. · 2019 · Econometrics · cited 84 times · AI summary available

   Abstract

   We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all …

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   Partial Identification with Noisy Covariates: A Robust Optimization Approach

   Wenshuo Guo, Mingzhang Yin, Yixin Wang et al. · 2022 · Machine Learning · cited 23 times · AI summary available

   Abstract

   Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, or only measurements of their proxies may be available. Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates. Moreover, without additional assumptions, the causal effects are not point-identifiable due to the noise in these measurements. To this end, we study the partial identification of causal effects given noisy covariates, under a user-specified assumption on the noise level. The key observation is that we can formulate the identification of the average treatm…

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   Functional Estimation under Proxy-Based Full-Law Identification

   Helen Guo, AmirEmad Ghassami, I. Shpitser et al. · 2026 · Not summarized yet

   Abstract

   We state general conditions under which the full-data law is identified in the presence of latent variables, leveraging key observed variables ("proxies") associated with unobserved variables. These assumptions extend those used in existing examples from the literature that recover the full-data law under relatively flexible model assumptions. We first describe graphical models compatible with our assumptions, and then consider target parameters that are functionals of the full-data law, developing estimating equations that give rise to consistent M-estimators using the observed data. Our approach replaces unobserved random variables appearing in full-data estimating equations with observed …

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   Proximal causal inference through cross-proxy balancing

   Grace V. Ringlein, T. Nguyen, Elizabeth A. Stuart et al. · 2026 · Not summarized yet

   Abstract

   Proximal causal inference identifies causal effects in the presence of unmeasured confounding by drawing on two sets of proxy variables. Identification typically utilizes bridge functions, defined as solutions to integral equations. However, the mechanism by which fitted bridge functions correct for confounding bias remains opaque, offering little to interpret, inspect or stress-test. We show that the defining equation of a treatment bridge function is already a balance condition, with a cross-proxy form: the weights are functions of the treatment confounding proxies and covariates, and they balance the outcome confounding proxies and covariates (i.e., reweighting the distribution in a parti…

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   Pro-Active Inference

   Skyler Wu, Yash Nair, Antoine Maechler et al. · 2026 · Not summarized yet

   Abstract

   Estimating a population mean is fundamental across scientific applications, but observing every outcome can be prohibitively expensive. Often, covariates and machine learning (ML) predictions are available for the full population, even when only a small subset of outcomes can be observed. How can we leverage this information to proactively select observations to label, update ML predictions as labels arrive, and construct statistically valid confidence intervals? We develop a general, without-replacement formulation of Pro-Active Inference (PAI), extending our earlier with-replacement approach and building on Active Statistical Inference. Rather than making fixed-order label-skip decisions, …

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   Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference

   Muwon Kwon, Peter M. Steiner · 2026 · Not summarized yet

   Abstract

   High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facilitates the use of machine learning (ML) for causal inference by mitigating regularization and overfitting bias, but comparatively less attention has been given to covariate selection in relation to the double robustness (DR) property possessed by some DML estimators. In particular, ML-based covariate selection may result in differential covariate selection or in misspecification of both models, thereby limiting the practical utility of the DR property. To address these issues,…

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   ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding

   Christoph Müller, Ayub Kharel, Alex Luedtke et al. · 2026 · Not summarized yet

   Abstract

   Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering causal estimands such as the conditional average treatment effect (CATE) requires solving an ill-posed integral equation that is data-hungry, hyperparameter-sensitive, and optimization-unstable. Bayesian inference for such models provides a desirable alternative, mitigating these difficulties by regularizing through the prior. However, computing a posterior is itself challengin…

Papers that cite it

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   Coarsening Latent-Class Probabilities: Directional Distortion and Coverage Loss

   M. T. Kurbucz · 2026 · cited 1 time · Not summarized yet

   Abstract

   Outcomes are regressed on a calibrated probability vector for unobserved class membership. Under a structural conditional mean excluding the score and conditional calibration, the observed-data model reduces to a partially linear regression. The probability vector is a Berkson-type surrogate for membership, so the effect vector $\tau$ is identified without attenuation. In practice the vector is often coarsened to a hard label - an argmax, a confidence threshold - and that label need not retain the Berkson property. For any coarsening the plug-in estimator converges to $\mathcal{A}\tau$, where $\mathcal{A}-I$ is determined by the regression of the discarded part of the score on the retained p…