28 citations · 127 across the 46 of their papers we have counts for
7 papers · 1 filter
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
Yang Cai, Alkis Kalavasis, Katerina Mamali +2
Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that…
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set . Kontonis, Tzamos, and Zampetakis (FOCS…
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas +1
In the classical setting of self-selection, the goal is to learn models, simultaneously from observations where is the output of one of under…
Computationally and Statistically Efficient Truncated Regression
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos +1
We provide a computationally and statistically efficient estimator for the classical problem of truncated linear regression, where the dependent variable and its co…
Estimation and Inference with Trees and Forests in High Dimensions
Vasilis Syrgkanis, Manolis Zampetakis
We analyze the finite sample mean squared error (MSE) performance of regression trees and forests in the high dimensional regime with binary features, under a sparsity constraint.…
Efficient Truncated Statistics with Unknown Truncation
Vasilis Kontonis, Christos Tzamos, Manolis Zampetakis
We study the problem of estimating the parameters of a Gaussian distribution when samples are only shown if they fall in some (unknown) subset . This core problem…
Efficient Statistics, in High Dimensions, from Truncated Samples
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos +1
We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate norm…