論文 - 岡　達志

Regression adjustment for estimating distributional treatment effects in randomized controlled trials

Oka T., Yasui S., Hayakawa Y., Byambadalai U.

Econometric Reviews 45 （ 1 ） 2 - 17 2026年

ISSN  07474938

　概要を見る

In this article, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment heterogeneity compared to average treatment effects. We propose a regression adjustment method that utilizes distributional regression and pre-treatment information, establishing theoretical efficiency gains without imposing restrictive distributional assumptions. We develop a practical inferential framework and demonstrate its advantages through extensive simulations. Analyzing water conservation policies, our method reveals that behavioral nudges systematically shift consumption from high to moderate levels. Examining health insurance coverage, we show the treatment reduces the probability of zero doctor visits by 6.6 percentage points while increasing the likelihood of 3-6 visits. In both applications, our regression adjustment method substantially improves precision and identifies treatment effects that were statistically insignificant under conventional approaches.

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Latent Variable Modeling for Robust Causal Effect Estimation

Morimura T., Oka T., Suzuki Y., Moriwaki D.

Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management    2179 - 2189 2025年11月

　概要を見る

Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing challenges posed by missing or unmeasured covariates. This paper proposes a new framework that integrates latent variable modeling into the double machine learning (DML) paradigm to enable robust causal effect estimation in the presence of such hidden factors. We consider two scenarios: one where a latent variable affects only the outcome, and another where it may influence both treatment and outcome. To ensure tractability, we incorporate latent variables only in the second stage of DML, separating representation learning from latent inference. We demonstrate the robustness and effectiveness of our method through extensive experiments on both synthetic and real-world datasets.

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QR.break: An R Package for Structural Breaks in Quantile Regression

Qu Z., Oka T., Messer S.

Journal of Econometric Methods 14 （ 1 ） 21 - 34 2025年01月

ISSN  21566674

　概要を見る

The QR.break package provides methods for detecting, estimating, and conducting inference on multiple structural breaks in linear quantile regression models, based on one or multiple quantiles and applicable to both time series and repeated cross-sectional data. The main function, rq.break(), returns testing and estimation results based on user specifications of the quantiles of interest, the maximum number of breaks allowed, and the minimum length of a single regime. This note outlines the underlying methods and explains how to use the main function with two datasets: A time series dataset on U.S. real GDP growth rates and a repeated cross-sectional dataset on youth drinking and driving behavior. Both datasets are included in the package available on CRAN.

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On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization

Byambadalai U., Hirata T., Oka T., Yasui S.

Proceedings of Machine Learning Research 267 2025年

　概要を見る

This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron’s biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and empirical analyses of microcredit programs highlight the practical advantages of our method.

Exploring Explanations Improves the Robustness of In-Context Learning

Honda U., Oka T.

Proceedings of the Annual Meeting of the Association for Computational Linguistics 1   23693 - 23714 2025年

ISSN  0736587X

　概要を見る

In-context learning (ICL) has emerged as a successful paradigm for leveraging large language models (LLMs). However, it often struggles to generalize beyond the distribution of the provided demonstrations. A recent advancement in enhancing robustness is ICL with explanations (X-ICL), which improves prediction reliability by guiding LLMs to understand and articulate the reasoning behind correct labels. Building on this approach, we introduce an advanced framework that extends X-ICL by systematically exploring explanations for all possible labels (X²-ICL), thereby enabling more comprehensive and robust decision-making. Experimental results on multiple natural language understanding datasets validate the effectiveness of X²-ICL, demonstrating significantly improved robustness to out-of-distribution data compared to the existing ICL approaches.

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Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding

Honda, U; Oka, T; Zhang, PA; Mita, M

TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS 12   1268 - 1289 2024年10月

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Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction

Byambadalai U., Oka T., Yasui S.

Proceedings of Machine Learning Research 235   5082 - 5113 2024年

　概要を見る

We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.

Quantile random-coefficient regression with interactive fixed effects: Heterogeneous group-level policy evaluation

Xu R., Gao J., Oka T., Whang Y.

Econometric Reviews 2024年

ISSN  07474938

　概要を見る

We propose a quantile random-coefficient regression with interactive fixed effects to study the effects of group-level policies that are heterogeneous across individuals. Our approach is the first to use a latent factor structure to handle the unobservable heterogeneities in the random coefficient. The asymptotic properties and an inferential method for the policy estimators are established. The model is applied to evaluate the effect of the minimum wage policy on earnings between 1967 and 1980 in the United States. Our results suggest that the minimum wage policy has significant and persistent positive effects on black workers and female workers up to the median. Our results also indicate that the policy helps reduce income disparity up to the median between two groups: black, female workers versus white, male workers. However, the policy is shown to have little effect on narrowing the income gap between low- and high-income workers within the subpopulations.

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Safe Collaborative Filtering

Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

12th International Conference on Learning Representations, ICLR 2024 2023年06月

　概要を見る

Excellent tail performance is crucial for modern machine learning tasks, such
as algorithmic fairness, class imbalance, and risk-sensitive decision making,
as it ensures the effective handling of challenging samples within a dataset.
Tail performance is also a vital determinant of success for personalised
recommender systems to reduce the risk of losing users with low satisfaction.
This study introduces a "safe" collaborative filtering method that prioritises
recommendation quality for less-satisfied users rather than focusing on the
average performance. Our approach minimises the conditional value at risk
(CVaR), which represents the average risk over the tails of users' loss. To
overcome computational challenges for web-scale recommender systems, we develop
a robust yet practical algorithm that extends the most scalable method,
implicit alternating least squares (iALS). Empirical evaluation on real-world
datasets demonstrates the excellent tail performance of our approach while
maintaining competitive computational efficiency.


Heterogeneous Impact of the Minimum Wage Implications for Changes in Betweenand Within-Group Inequality

Oka T., Yamada K.

Journal of Human Resources （Journal of Human Resources）  58 （ 1 ） 335 - 362 2023年

ISSN  0022166X

　概要を見る

In the United States, most of the workers who earn at or below the minimum wage are either less educated, young, or female. We examine the extent to which the minimum wage influences the wage differential among workers with different observed characteristics and the wage differential among workers with the same observed characteristics. Our results suggest that changes in the real value of the minimum wage account in part for the patterns of changes in education, experience, and gender wage differentials and for most of the changes in within-group wage differentials for workers with lower levels of experience.

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Semiparametric Single-Index Estimation for Average Treatment Effects

Difang Huang, Jiti Gao, Tatsushi Oka

2022年06月

　概要を見る

We propose a semiparametric method to estimate the average treatment effect
under the assumption of unconfoundedness given observational data. Our
estimation method alleviates misspecification issues of the propensity score
function by estimating the single-index link function involved through Hermite
polynomials. Our approach is computationally tractable and allows for
moderately large dimension covariates. We provide the large sample properties
of the estimator and show its validity. Also, the average treatment effect
estimator achieves the parametric rate and asymptotic normality. Our extensive
Monte Carlo study shows that the proposed estimator is valid in finite samples.
We also provide an empirical analysis on the effect of maternal smoking on
babies' birth weight and the effect of job training program on future earnings.


Bivariate Distribution Regression with Application to Insurance Data

Wang, Y. and Oka, T. and Zhu, D.

arXiv （Insurance: Mathematics and Economics）  113   215 - 232 2022年

ISSN  23318422

　概要を見る

Understanding variable dependence, particularly eliciting their statistical
properties given a set of covariates, provides the mathematical foundation in
practical operations management such as risk analysis and decision making given
observed circumstances. This article presents an estimation method for modeling
the conditional joint distribution of bivariate outcomes based on the
distribution regression and factorization methods. This method is considered
semiparametric in that it allows for flexible modeling of both the marginal and
joint distributions conditional on covariates without imposing global
parametric assumptions across the entire distribution. In contrast to existing
parametric approaches, our method can accommodate discrete, continuous, or
mixed variables, and provides a simple yet effective way to capture
distributional dependence structures between bivariate outcomes and covariates.
Various simulation results confirm that our method can perform similarly or
better in finite samples compared to the alternative methods. In an application
to the study of a motor third-part liability insurance portfolio, the proposed
method effectively estimates risk measures such as the conditional
Value-at-Risks and Expexted Sortfall. This result suggests that this
semiparametric approach can serve as an alternative in insurance risk
management.


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The effect of human mobility restrictions on the COVID-19 transmission network in China

Oka T., Wei W., Zhu D.

PLoS ONE （PLoS ONE）  16 （ 7 July 2021 ）  2021年07月

　概要を見る

Background COVID-19 poses a severe threat worldwide. This study analyzes its propagation and evaluates statistically the effect of mobility restriction policies on the spread of the disease. Methods We apply a variation of the stochastic Susceptible-Infectious-Recovered model to describe the temporal-spatial evolution of the disease across 33 provincial regions in China, where the disease was first identified. We employ Bayesian Markov Chain Monte-Carlo methods to estimate the model and to characterize a dynamic transmission network, which enables us to evaluate the effectiveness of various local and national policies. Results The spread of the disease in China was predominantly driven by community transmission within regions, which dropped substantially after local governments imposed various lockdown policies. Further, Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020. The transmission from these epicenters substantially declined following the introduction of mobility restrictions across regions. Conclusions The spatial transmission network is able to differentiate the effect of the local lockdown policies and the cross-region mobility restrictions. We conclude that both are important policy tools for curbing the disease transmission. The coordination between central and local governments is important in suppressing the spread of infectious diseases.

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Inference related to common breaks in a multivariate system with joined segmented trends with applications to global and hemispheric temperatures

Kim D., Oka T., Estrada F., Perron P.

Journal of Econometrics （Journal of Econometrics）  214 （ 1 ） 130 - 152 2020年01月

ISSN  03044076

　概要を見る

What transpires from recent research is that temperatures and radiative forcing seem to be characterized by a linear trend with two changes in the rate of growth. The first occurs in the early 60s and indicates a very large increase in the rate of growth of both temperature and radiative forcing series. This was termed as the “onset of sustained global warming”. The second is related to the more recent so-called hiatus period, which suggests that temperatures and total radiative forcing have increased less rapidly since the mid-90s compared to the larger rate of increase from 1960 to 1990. There are two issues that remain unresolved. The first is whether the breaks in the slope of the trend functions of temperatures and radiative forcing are common. This is important because common breaks coupled with the basic science of climate change would strongly suggest a causal effect from anthropogenic factors to temperatures. The second issue relates to establishing formally via a proper testing procedure that takes into account the noise in the series, whether there was indeed a ‘hiatus period’ for temperatures since the mid 90s. This is important because such a test would counter the widely held view that the hiatus is the product of natural internal variability. Our paper provides tests related to both issues. The results show that the breaks in temperatures and radiative forcing are common and that the hiatus is characterized by a significant decrease in their rate of growth. The statistical results are of independent interest and applicable more generally.

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Indirect inference with a non-smooth criterion function

Frazier D.T., Oka T., Zhu D.

Journal of Econometrics （Journal of Econometrics）  212 （ 2 ） 623 - 645 2019年10月

ISSN  03044076

　概要を見る

Indirect inference requires simulating realizations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of derivative-based optimization routines. Using a change of variables technique, we propose a novel simulation algorithm that alleviates the discontinuities inherent in such indirect inference criterion functions, and permits the application of derivative-based optimization routines to estimate the unknown model parameters. Unlike competing approaches, this approach does not rely on kernel smoothing or bandwidth parameters. Several Monte Carlo examples that have featured in the literature on indirect inference with discontinuous outcomes illustrate the approach, and demonstrate the superior performance of this approach over existing alternatives.

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Testing for common breaks in a multiple equations system

Oka T., Perron P.

Journal of Econometrics （Journal of Econometrics）  204 （ 1 ） 66 - 85 2018年05月

ISSN  03044076

　概要を見る

The issue addressed in this paper is that of testing for common breaks across or within equations of a multivariate system. Our framework is very general and allows integrated regressors and trends as well as stationary regressors. The null hypothesis is that breaks in different parameters occur at common locations and are separated by some positive fraction of the sample size unless they occur across different equations. Under the alternative hypothesis, the break dates across parameters are not the same and also need not be separated by a positive fraction of the sample size whether within or across equations. The test considered is the quasi-likelihood ratio test assuming normal errors, though as usual the limit distribution of the test remains valid with non-normal errors. Of independent interest, we provide results about the rate of convergence of the estimates when searching over all possible partitions subject only to the requirement that each regime contains at least as many observations as some positive fraction of the sample size, allowing break dates not separated by a positive fraction of the sample size across equations. Simulations show that the test has good finite sample properties. We also provide an application to issues related to level shifts and persistence for various measures of inflation to illustrate its usefulness.

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Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Callaway, B. and Li, T. and Oka, T.

Journal of Econometrics （Journal of Econometrics）  206 （ 2 ） 395 - 413 2018年

ISSN  03044076

　概要を見る

This paper shows that the Conditional Quantile Treatment Effect on the Treated is identified under (i) a Conditional Distributional Difference in Differences assumption and (ii) a new assumption that the dependence (the copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group. We consider estimation and inference with discrete covariates and propose a uniform inference procedure based on the exchangeable bootstrap. Finally, we estimate the effect of increasing the minimum wage on the distribution of earnings for subgroups defined by race, gender, and education.

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The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series

Han H., Linton O., Oka T., Whang Y.J.

Journal of Econometrics （Journal of Econometrics）  193 （ 1 ） 251 - 270 2016年07月

ISSN  03044076

　概要を見る

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ a stationary bootstrap procedure; we establish consistency of this bootstrap. Also, we consider a self-normalized approach, which yields an asymptotically pivotal statistic under the null hypothesis of no predictability. We provide simulation studies and two empirical applications. First, we use the cross-quantilogram to detect predictability from stock variance to excess stock return. Compared to existing tools used in the literature of stock return predictability, our method provides a more complete relationship between a predictor and stock return. Second, we investigate the systemic risk of individual financial institutions, such as JP Morgan Chase, Morgan Stanley and AIG.

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Set identification of the censored quantile regression model for short panels with fixed effects

Li T., Oka T.

Journal of Econometrics （Journal of Econometrics）  188 （ 2 ） 363 - 377 2015年10月

ISSN  03044076

　概要を見る

This paper studies identification and estimation of a censored quantile regression model for short panel data with fixed effects. Using the redistribution-of-mass idea, we obtain bounds on the conditional distribution of differences of the model across periods, under conditional quantile restrictions together with a weak conditional independence assumption along the lines of Rosen (2012). The inversion of the distribution bounds characterizes the sharp identified set via a set of inequalities based on conditional quantile functions. Due to the presence of censoring, some of the inequalities defining the identified set hold trivially and have no identification power. Moreover, those trivial inequalities cause a difficulty in estimating the identified set. To deal with the issue, we propose a two-step estimation method, where the first step consists of excluding trivial inequalities and the second step performs minimization of a convex criterion function using the remaining inequalities. We establish asymptotic properties of the set estimator and also consider sufficient conditions under which point identification can be attained.

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Divorce law reforms and divorce rates in the USA: An interactive fixed-effects approach

Kim D., Oka T.

Journal of Applied Econometrics （Journal of Applied Econometrics）  29 （ 2 ） 231 - 245 2014年03月

ISSN  08837252

　概要を見る

SUMMARY: This paper estimates the effects of unilateral divorce laws on divorce rates in the USA from a panel of state-level divorce rates. We use the interactive fixed-effects model to address the issue of endogeneity due to the association between cross-state unobserved heterogeneity and divorce law reforms. We document that earlier studies in the literature do not fully control for unobserved heterogeneity and result in mixed empirical evidence on the effects of divorce law reforms. While reconciling these conflicting results, our results suggest that divorce law reforms have temporal positive effects on divorce rates, thus confirming the 2006 findings of Wolfers. Via simulation experiments, we assess the degree to which faulty inclusion or faulty exclusion of interactive fixed effects affects the policy effect estimators. Our results suggest that faulty inclusion only results in efficiency loss whereas faulty exclusion causes bias. © 2013 John Wiley & Sons, Ltd.

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Estimating structural changes in regression quantiles

Oka T., Qu Z.

Journal of Econometrics （Journal of Econometrics）  162 （ 2 ） 248 - 267 2011年06月

ISSN  03044076

　概要を見る

This paper considers the estimation of multiple structural changes occurring at unknown dates in one or multiple conditional quantile functions. The analysis covers time series models as well as models with repeated cross-sections. We estimate the break dates and other parameters jointly by minimizing the check function over all permissible break dates. The limiting distribution of the estimator is derived and the coverage property of the resulting confidence interval is assessed via simulations. A procedure to determine the number of breaks is also discussed. Empirical applications to the quarterly US real GDP growth rate and the underage drunk driving data suggest that the method can deliver more informative results than the analysis of the conditional mean function alone. © 2011 Elsevier B.V. All rights reserved.

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Juvenile crime and punishment: Evidence from Japan

Oka T.

Applied Economics （Applied Economics）  41 （ 24 ） 3103 - 3115 2009年11月

ISSN  00036846

　概要を見る

Over the last decade, juvenile crime has become a serious social problem in Japan. The Juvenile Law was revised in 2001 to impose harsher punishment on juvenile offenders. This revision makes it possible to impose criminal punishment on 14- and 15-year-old criminal offenders, while those offenders aged 16-19 have always faced criminal punishment, both before and after the revision. Using this revision as a natural experiment, this study conducts a difference-in-differences estimation to examine the effect of punishment on juvenile crime. The analysis provides evidence that punishment can deter juvenile crime. In addition, this research examines the criminal behaviour of 13-year-olds, who face no change in punishment, but who soon will in the near future. The results suggest that the revision also had a negative impact on the criminal behaviour of these younger offenders. © 2009 Taylor & Francis.

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