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Found 10 papers, 3 papers with code
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.
On Computationally Efficient Learning of Exponential Family Distributions
While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard.
Group Fairness with Uncertainty in Sensitive Attributes
To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes.
On counterfactual inference with unobserved confounding
Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes.
Optimal Compression of Locally Differentially Private Mechanisms
Compressing the output of \epsilon-locally differentially private (LDP) randomizers naively leads to suboptimal utility.
Selective Regression Under Fairness Criteria
Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient.
A Computationally Efficient Method for Learning Exponential Family Distributions
In this work, we propose a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions.
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Our main result strengthens these prior results by showing that under a different expert-driven structural knowledge -- that one variable is a direct causal parent of treatment variable -- remarkably, testing for subsets (not involving the known parent variable) that are valid back-doors is equivalent to an invariance test.
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias.
On Learning Continuous Pairwise Markov Random Fields
We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-valued variables from i. i. d samples.
