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Found 20 papers, 3 papers with code
Does AI help humans make better decisions? A methodological framework for experimental evaluation
We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions.
The Cram Method for Efficient Simultaneous Learning and Evaluation
Our extensive simulation studies show that, when compared to sample-splitting, cramming reduces the evaluation standard error by more than 40% while improving the performance of learned policy.
Individualized Policy Evaluation and Learning under Clustered Network Interference
Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR.
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it.
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm.
Distributionally Robust Causal Inference with Observational Data
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders.
Race and ethnicity data for first, middle, and last names
Counts can then be normalized row-wise or column-wise to obtain conditional probabilities of race given name or name given race.
Policy Learning with Asymmetric Counterfactual Utilities
We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes.
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements
To address the missing surname problem, we supplement the Census surname data with additional data on last, first, and middle names taken from the voter files of six Southern states where self-reported race is available.
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects.
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
We propose a new hypothesis testing approach based on the conditional randomization test to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors?
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
We extend this approach to common and important settings where humans make decisions with the aid of algorithmic recommendations.
Estimating Average Treatment Effects with Support Vector Machines
Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature.
Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans
Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevant space of redistricting plans better than the existing Markov chain Monte Carlo (MCMC) algorithms that generate plans sequentially.
Applications Computers and Society Probability
Principal Fairness for Human and Algorithmic Decision-Making
Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making.
Keyword Assisted Topic Models
An important advantage of the proposed keyword assisted topic model (keyATM) is that the specification of keywords requires researchers to label topics prior to fitting a model to the data.
Large-scale text processing pipeline with Apache Spark
In this paper, we evaluate Apache Spark for a data-intensive machine learning problem.
Experimental Evaluation of Individualized Treatment Rules
We extend our methodology to a common setting, in which the same experimental data is used to both estimate and evaluate ITRs.
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score
We first use a class of penalized M-estimators for the propensity score and outcome models.
