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Found 3 papers, 3 papers with code
Valid causal inference with unobserved confounding in high-dimensional settings
We propose uncertainty intervals which allow for unobserved confounding, and show that the resulting inference is valid when the amount of unobserved confounding is small relative to the sample size; the latter is formalized in terms of convergence rates.
Convolutional neural networks for valid and efficient causal inference
In an application where we want to estimate the effect of early retirement on a health outcome, we propose to use CNN to control for time-structured covariates.
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and nontreated groups.
Methodology
