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Found 47 papers, 28 papers with code
Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap
In this paper, we re-visit the task of off-policy evaluation in MDPs under a weaker notion of distributional overlap, and introduce a class of truncated doubly robust (TDR) estimators which we find to perform well in this setting.
Policy Learning under Biased Sample Selection
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population.
Experimenting under Stochastic Congestion
We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply and/or demand.
Learning from a Biased Sample
Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling.
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
A related approach, called "model-based forests", that is geared towards randomized trials and simultaneously captures effects of both prognostic and predictive variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a modular implementation in the R package model4you.
Policy Learning with Competing Agents
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat.
Partial Likelihood Thompson Sampling
We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease.
Thompson Sampling with Unrestricted Delays
We investigate properties of Thompson Sampling in the stochastic multi-armed bandit problem with delayed feedback.
Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions.
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules.
Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorability
We consider off-policy evaluation of dynamic treatment rules under sequential ignorability, given an assumption that the underlying system can be modeled as a partially observed Markov decision process (POMDP).
Treatment Effects in Market Equilibrium
When randomized trials are run in a marketplace equilibriated by prices, interference arises.
Weak Signal Asymptotics for Sequentially Randomized Experiments
We use the lens of weak signal asymptotics to study a class of sequentially randomized experiments, including those that arise in solving multi-armed bandit problems.
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
Confidence Intervals for Policy Evaluation in Adaptive Experiments
In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero.
Doubly robust treatment effect estimation with missing attributes
We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing data repeatedly perform better than their non-doubly robust baselines; for example, doubly robust generalized propensity score methods beat inverse-weighting with the generalized propensity score.
Methodology 93C41, 62G35, 62F35, 62P10
Smoothness-Adaptive Contextual Bandits
In this work, we consider a framework where the smoothness of payoff functions is not known, and study when and how algorithms may adapt to unknown smoothness.
Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input.
Covariate-Powered Empirical Bayes Estimation
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information.
Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs
Identifying heterogeneity in a population's response to a health or policy intervention is crucial for evaluating and informing policy decisions.
Methodology
Learning When-to-Treat Policies
Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment.
Sparsity Double Robust Inference of Average Treatment Effects
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice.
Statistics Theory Econometrics Methodology Statistics Theory
Estimating Treatment Effects with Causal Forests: An Application
We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.
Methodology
Confidence Intervals for Nonparametric Empirical Bayes Analysis
In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution.
Methodology
Synthetic Difference in Differences
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.
Methodology
Offline Multi-Action Policy Learning: Generalization and Optimization
In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action.
Local Linear Forests
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects.
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal.
Augmented Minimax Linear Estimation
Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function.
Methodology 62F12
Optimized Regression Discontinuity Designs
The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting non-parametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest.
Methodology
Policy Learning with Observational Data
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints.
Generalized Random Forests
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations.
High-dimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect.
Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables.
Methodology Econometrics Statistics Theory Statistics Theory
Data Augmentation via Levy Processes
The framework imagines data as being drawn from a slice of a Levy process.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity.
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model.
Adaptive Concentration of Regression Trees, with Application to Random Forests
To support our analysis we introduce a notion of adaptive concentration for regression trees.
The Statistics of Streaming Sparse Regression
We present a sparse analogue to stochastic gradient descent that is guaranteed to perform well under similar conditions to the lasso.
Bootstrap-Based Regularization for Low-Rank Matrix Estimation
In the simplest case, with an isotropic noise model, our method is equivalent to a classical singular value shrinkage estimator.
Altitude Training: Strong Bounds for Single-Layer Dropout
Dropout training, originally designed for deep neural networks, has been successful on high-dimensional single-layer natural language tasks.
Asymptotic Theory for Random Forests
Random forests have proven to be reliable predictive algorithms in many application areas.
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
Our work builds on variance estimates for bagging proposed by Efron (1992, 2012) that are based on the jackknife and the infinitesimal jackknife (IJ).
Feedback Detection for Live Predictors
A predictor that is deployed in a live production system may perturb the features it uses to make predictions.
Weakly supervised clustering: Learning fine-grained signals from coarse labels
Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations.
Dropout Training as Adaptive Regularization
Dropout and other feature noising schemes control overfitting by artificially corrupting the training data.
