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Found 17 papers, 7 papers with code
Source Condition Double Robust Inference on Functionals of Inverse Problems
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems.
Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach
The labeled data distribution may substantially differ from the full population, especially when the historical decisions and the target outcome can be simultaneously affected by some unobserved factors.
Online Joint Assortment-Inventory Optimization under MNL Choices
We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori.
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Inference on Strongly Identified Functionals of Weakly Identified Functions
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e. g., average causal effects) of nuisance function (e. g., NPIV regression) defined by conditional moment restrictions.
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Thanks to a localization technique, LDR$^2$OPE only requires fitting a small number of regressions, just like DR methods for standard OPE.
Long-term Causal Inference Under Persistent Confounding via Data Combination
In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i. e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature.
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
Previous work has relied on completeness conditions on these functions to identify the causal parameters and required uniqueness assumptions in estimation, and they also focused on parametric estimation of bridge functions.
Fast Rates for Contextual Linear Optimization
While one may use off-the-shelf machine learning methods to separately learn a predictive model and plug it in, a variety of recent methods instead integrate estimation and optimization by fitting the model to directly optimize downstream decision performance.
Stochastic Optimization Forests
We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e. g., product characteristics) to improve decision making with uncertain variables (e. g., demand).
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
However, there is often an abundance of observations on surrogate outcomes not of primary interest, such as short-term health effects or online-ad click-through.
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated.
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
We study a nonparametric contextual bandit problem where the expected reward functions belong to a H\"older class with smoothness parameter $\beta$.
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in the data.
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership.
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders.
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Valid causal inference in observational studies often requires controlling for confounders.
