Doubly Robust Inference in Causal Latent Factor Models
<|reference_start|>Doubly Robust Inference in Causal Latent Factor Models: This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-sample and asymptotic guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate. Simulation results demonstrate the practical relevance of the formal properties of the estimators analyzed in this article.<|reference_end|>
@article{abadie2024doubly,
    title={Doubly Robust Inference in Causal Latent Factor Models},
    author={Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah},
    journal={arXiv preprint arXiv:2402.11652},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11652},
    primaryClass={econ.EM cs.LG stat.ME stat.ML}
}