1 code implementation • 25 Apr 2024 • Michael Lingzhi Li, Kosuke Imai
In this paper, we demonstrate that Neyman's methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning algorithms.
no code implementations • 18 Mar 2024 • Eli Ben-Michael, D. James Greiner, Melody Huang, Kosuke Imai, Zhichao Jiang, Sooahn Shin
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.
no code implementations • 11 Mar 2024 • Zeyang Jia, Kosuke Imai, Michael Lingzhi Li
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.
no code implementations • 4 Nov 2023 • Yi Zhang, Kosuke Imai
Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR.
no code implementations • 12 Oct 2023 • Michael Lingzhi Li, Kosuke Imai
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.
no code implementations • 17 Jul 2023 • Zeyang Jia, Eli Ben-Michael, Kosuke Imai
First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm.
no code implementations • 15 Oct 2022 • Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders.
no code implementations • 26 Aug 2022 • Evan T. R. Rosenman, Santiago Olivella, Kosuke Imai
Counts can then be normalized row-wise or column-wise to obtain conditional probabilities of race given name or name given race.
no code implementations • 21 Jun 2022 • Eli Ben-Michael, Kosuke Imai, Zhichao Jiang
We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes.
no code implementations • 12 May 2022 • Kosuke Imai, Santiago Olivella, Evan T. R. Rosenman
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.
no code implementations • 28 Mar 2022 • Kosuke Imai, Michael Lingzhi Li
In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects.
1 code implementation • 20 Jan 2022 • Dae Woong Ham, Kosuke Imai, Lucas Janson
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?
no code implementations • 22 Sep 2021 • Eli Ben-Michael, D. James Greiner, Kosuke Imai, Zhichao Jiang
We extend this approach to common and important settings where humans make decisions with the aid of algorithmic recommendations.
no code implementations • 23 Feb 2021 • Alexander Tarr, Kosuke Imai
Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature.
2 code implementations • 13 Aug 2020 • Cory McCartan, Kosuke Imai
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
no code implementations • 21 May 2020 • Kosuke Imai, Zhichao Jiang
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.
1 code implementation • 13 Apr 2020 • Shusei Eshima, Kosuke Imai, Tomoya Sasaki
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.
no code implementations • 2 Dec 2019 • Alexey Svyatkovskiy, Kosuke Imai, Mary Kroeger, Yuki Shiraito
In this paper, we evaluate Apache Spark for a data-intensive machine learning problem.
no code implementations • 15 Oct 2019 • Kosuke Imai, Zhichao Jiang
This commentary has two goals.
no code implementations • 14 May 2019 • Kosuke Imai, Michael Lingzhi Li
We extend our methodology to a common setting, in which the same experimental data is used to both estimate and evaluate ITRs.
no code implementations • 20 Dec 2018 • Yang Ning, Sida Peng, Kosuke Imai
We first use a class of penalized M-estimators for the propensity score and outcome models.