Papers - Hoshino, Takahiro

Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference

Takahiro Hoshino

Journal of the American Statistical Association (Journal of the American Statistical Association)  108 ( 504 ) 1189 - 1204 2013.12

Research paper (scientific journal), Single Work, Accepted,  ISSN  01621459

　View Summary

We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on potential outcomes. The model uses the probit stick-breaking process mixture proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no parametric model assumption for the assignment model and conditional distribution of the covariate vector. The proposed estimation method is more robust than maximum likelihood estimation, in that it does not require knowledge of the full joint distribution of potential outcomes, covariates, and assignments. In addition, the method is more efficient than fully nonparametric Bayes methods. We apply this model to infer the differential effects of cognitive and noncognitive skills on the wages of production and nonproduction workers using panel data from the National Longitudinal Survey of Youth in 1979. The study also presents the causal effect of online word-of-mouth onWeb site browsing behavior. Supplementary materials for this article are available online. © 2013 American Statistical Association.

• Access to Document (DOI)

Real-Time Survey of Vaccine Safety of the mRNA-1273 SARS-CoV-2 Vaccine in Workplace Vaccination at Keio University

Kaho Okumura, Azusa Hara, Isa Inada, Daisuke Sugiyama, Takahiro Hoshino, Takahiro Yakoh, Hirokatsu Yokoyama, Hisashi Urushihara

Vaccines (Vaccines)  10 ( 9 ) 1461 2022.09

Accepted

　View Summary

The mRNA-1273 Moderna COVID-19 vaccine was introduced to combat the COVID-19 global pandemic in 2020. Although the safety of the vaccine has been investigated worldwide, real-world safety data is scarce in Japan. An online, real-time survey of adverse events following immunization (AEFIs) with mRNA-1273 was conducted in the setting of a workplace vaccination program at the School of Pharmacy, Keio University from 26 June 2021, to 11 June 2022. Participants were requested to take four surveys during a seven-day follow-up period after each of the first, second, and third booster doses. The maximum number of responses, from 301 respondents, was obtained on day 0 (vaccination date) for the first dose. 98% of respondents reported local and systemic AEFIs for the second dose on day 1. No noticeable difference in local reactions was seen among the three doses. Females reported more AEFIs than males, and the young group (18–29 years) reported a higher rate than the middle age group (≥30 years) after the first dose. Age and gender differences in rates decreased at the second and third doses. This survey confirmed that the safety profile of mRNA-1273 in a real-world setting was similar to that derived from the clinical trials, and that the agent was well-tolerated.

• Access to Document (DOI)

Joint Modeling of Effects of Customer Tier Program on Customer Purchase Duration and Purchase Amount

Kazuki Nishio, Takahiro Hoshino

Journal of Retailing and Consumer Services (Journal of Retailing and Consumer Services)  66 2022.05

Last author, Corresponding author, Accepted,  ISSN  09696989

　View Summary

Nowadays, many supermarkets implement a customer tier program to increase their profits because it is expected to raise customers' willingness to purchase by setting thresholds. However, designing an appropriate program is difficult because each customer's heterogeneous purchase behavior is difficult to capture. Therefore, we simultaneously modeled purchase frequency and amount through a marked point process approach while considering program effects and customer characteristics. The results clarified that the points pressure effect was particularly strong among customers who originally visited the store infrequently and had not attained the threshold set up in the customer tier program many times in the past. In addition, we found that the three-tier customer tier program was superior to the two-tier program with respect to the operating income in supermarkets.

• Access to Document (DOI)

Estimation of Local Average Treatment Effect by Data Combination

Kazuhiko Shinoda, Takahiro Hoshino

Proceedings of the AAAI Conference on Artificial Intelligence 36 2022

Last author, Accepted

　View Summary

It is important to estimate the local average treatment effect (LATE) when
compliance with a treatment assignment is incomplete. The previously proposed
methods for LATE estimation required all relevant variables to be jointly
observed in a single dataset; however, it is sometimes difficult or even
impossible to collect such data in many real-world problems for technical or
privacy reasons. We consider a novel problem setting in which LATE, as a
function of covariates, is nonparametrically identified from the combination of
separately observed datasets. For estimation, we show that the direct least
squares method, which was originally developed for estimating the average
treatment effect under complete compliance, is applicable to our setting.
However, model selection and hyperparameter tuning for the direct least squares
estimator can be unstable in practice since it is defined as a solution to the
minimax problem. We then propose a weighted least squares estimator that
enables simpler model selection by avoiding the minimax objective formulation.
Unlike the inverse probability weighted (IPW) estimator, the proposed estimator
directly uses the pre-estimated weight without inversion, avoiding the problems
caused by the IPW methods. We demonstrate the effectiveness of our method
through experiments using synthetic and real-world datasets.


Effects of web-based learning for nurses on their care for pregnant women with hiesho (sensitivity of hands or feet to cold): A randomized controlled trial

Sachiyo Nakamura, Shoko Takeuchi, Takahiro Hoshino, Naoko Okubo, Shigeko Horiuchi

Japanese Journal of Nursing Science in press 2022

Accepted

Semiparametric Bayes instrumental variable estimation with many weak instruments

Ryo Kato, Takahiro Hoshino

Stat (Wiley)  10 ( 1 ) e350 2021.12

Last author, Corresponding author, Accepted,  ISSN  2049-1573

　View Summary

We develop a new semiparametric Bayes instrumental variables estimation method. We employ the form of the regression function of the first-stage equation, and the disturbances are modeled nonparametrically to achieve better predictive power of the endogenous variables, whereas we use parametric formulation in the second-stage equation, which is of interest in inference. Our simulation studies show that under small sample sizes, the proposed method obtains more efficient estimates and very precise credible intervals compared with existing IV methods with smaller mean squared error. We applied our proposed method to a Mendelian randomization dataset where a large number of instruments are available and semiparametric specification is appropriate. We obtained statistically significant results that cannot be obtained by the existing methods, including standard Bayesian IV.

• Access to Document (DOI)

Kernel canonical correlation analysis for data combination of multiple-source datasets

Masaki Mitsuhiro, Takahiro Hoshino

Japanese Journal of Statistics and Data Science (Springer Science and Business Media LLC)  3 ( 2 ) 669 - 691 2020.12

Last author, Corresponding author, Accepted,  ISSN  2520-8756

• Access to Document (DOI)

Positive-Unlabelled Survival Data Analysis

Tomoki Toyabe, Yasuhiro Hasegawa, Takahiro Hoshino

 2020.11

　View Summary

In this paper, we consider a novel framework of positive-unlabeled data in
which as positive data survival times are observed for subjects who have events
during the observation time as positive data and as unlabeled data censoring
times are observed but whether the event occurs or not are unknown for some
subjects. We consider two cases: (1) when censoring time is observed in
positive data, and (2) when it is not observed. For both cases, we developed
parametric models, nonparametric models, and machine learning models and the
estimation strategies for these models. Simulation studies show that under this
data setup, traditional survival analysis may yield severely biased results,
while the proposed estimation method can provide valid results.


The impact of competitors store flyer advertisement on EDLP/HiLo chain performance in highly competitive retail market: GPS information and POS data approach in Japan

KATO Ryo and HOSHINO Takahiro

Journal of Advertising 48 ( 5 ) 569 - 587 2020.03

Research paper (scientific journal), Single Work, Accepted

• Access to Document (DOI)

RFMC分析におけるClumpiness 指標の拡張と自社顧客の行動予測への応用：Clumpiness を活用した離脱時期と競合利用の予測手法の提案

星野 崇宏, 新美潤一郎

行動計量学 47 ( 1 ) 27 - 40 2020.03

Last author, Corresponding author, Accepted

Dynamic two stage modeling for category-level and brand-level purchases using potential outcome approach with Bayes inference

Kei Miyazaki, Takahiro Hoshino, Ulf Bockenholt

Journal of Business & Economic Statistics (Journal of Business and Economic Statistics)  in press 2020

Research paper (scientific journal), Joint Work, Accepted,  ISSN  07350015

　View Summary

© 2020, © 2020 American Statistical Association. We propose an econometric two-stage model for category-level purchase and brand-level purchase that allows for simultaneous brand purchases in the analysis of scanner panel data. The proposed model formulation is consistent with the traditional theory of consumer behavior. We conduct Bayesian estimation with the Markov chain Monte Carlo algorithm for our proposed model. The simulation studies show that previously proposed related models can cause severe bias in predicting future brand choices, while the proposed method can effectively predict them. Additionally in a marketing application, the proposed method can examine brand switching behaviors that existing methods cannot. Moreover, we show that the prediction accuracy of the proposed method is higher than that of existing methods.

• Access to Document (DOI)

Parametric identification of the joint distribution of the potential outcomes

Keisuke Takahata, Takahiro Hoshino

Stat (Stat)  9 ( 1 ) e254 - e254 2020

Last author, Corresponding author, Accepted

　View Summary

We show the identification of the joint distribution of the potential outcomes under various parametric specifications. The key factor of the identification is the nonnormality of the distribution of the observed variables, with which we can obtain information of higher order moments that are not determined only by mean and variance. In particular, we show the identification of the joint distribution of the potential outcomes when it is specified by a normal mixture. Because any continuous distribution can be well approximated by a finite mixture distribution, our result may cover a wide class of distributions. The identification results derived are useful for estimating quantile treatment effects, causal mediation effects, and heterogeneous treatment effects, which cannot be estimated even if the unconfoundedness assumption is satisfied.

• Access to Document (DOI)

Prediction of the Customer Activity with Using the Improved Measure of Clumpiness

Junichiro NIIMI, Takahiro HOSHINO

Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) (Behaviormetric Society of Japan)  47 ( 1 ) 27 - 40 2020

ISSN  0385-5481

• Access to Document (DOI)

Fatigue-Aware Ad Creative Selection

Daisuke Moriwaki, Komei Fujita, Shota Yasui, Takahiro Hoshino

 2019.08

　View Summary

In online display advertising, selecting the most effective ad creative (ad
image) for each impression is a crucial task for DSPs (Demand-Side Platforms)
to fulfill their goals (click-through rate, number of conversions, revenue, and
brand improvement). As widely recognized in the marketing literature, the
effect of ad creative changes with the number of repetitive ad exposures. In
this study, we propose an efficient and easy-to-implement ad creative selection
algorithm that explicitly considers user's psychological status when selecting
ad creatives. The proposed system was deployed in a real-world production
environment and tested against the baseline algorithms. The results show
superiority of the proposed algorithm.


Influenza vaccination effectiveness for people aged under 65 years in Japan, 2013/2014 season: Application of a doubly robust method to a large-scale, real-world dataset

Shibata N., Kimura S., Hoshino T., Urushihara H.

BMC Infectious Diseases (BMC Infectious Diseases)  19 ( 1 ) 586 2019.07

　View Summary

© 2019 The Author(s). Background: Influenza vaccination is recognized as a primary public health intervention which prevents the illness of patients and relieves the societal burdens of influenza for medical community as well as the economy. To date, no effectiveness study of influenza vaccination has been conducted including a large population with a wide age span, in Japan. Here, we evaluated the clinical effectiveness of influenza vaccination in a large Japanese population. Methods: We conducted a cohort study using a large-scale claims database for employee health care insurance plans. Vaccination status was identified using plan records for influenza vaccination subsidies. We excluded people aged 65 years or more because of the unavailability of vaccination records. Effectiveness of vaccination in preventing influenza and its complication was evaluated with doubly robust methods using inversed probability treatment weighting to adjust health conscious behaviours and other confounders. Results: During the 2013/2014 influenza season, 369,425 subjects with age range from 1 to 64 years were eligible. Vaccination rate was 39.5% and an estimated odds ratio (OR) for influenza onset was 0.775 after doubly robust adjustment. Age-stratified ORs were significantly reduced in all age groups; lowest in subjects aged 1 to 4 years (0.600) and highest in those aged 13 to 19 (0.938). ORs for all the influenza complication outcomes were also statistically significant (0.403-0.709). Conclusions: We confirmed the clinical effectiveness of influenza vaccination in people aged 1 to 64 years. Influenza vaccination significantly prevented influenza onset and was more effective in reducing secondary risks of influenza complications.

• Access to Document (DOI)

Influenza Vaccination Effectiveness in Japan, 2013/2014 Season: an Application of Doubly Robust Method to Large-scale, Real-world Dataset

Natsumi Shibata, Shinya Kimura, Takahiro Hoshino, Hisashi Urushihara

BMC Infectious Diseases 19 ( 586 )  2019.06

Accepted

マーケティングと行動経済学：二重過程理論と文脈効果に関するレビューと購買履歴データからの実証分析

星野 崇宏・竹内 真登

行動経済学 12 ( 1 ) 51 - 61 2019.03

Single Work, Accepted

• Access to Document (DOI)

Doubly robust-type estimation of population moments and parameters in biased sampling

Shimizu Y., Hoshino T.

Stat (Stat)  8 ( 1 )  2019.01

　View Summary

We propose an estimation method of population moments or parameters in “biased sampling data” in which for some units of data, not only the variables of interest but also the covariates have missing observations, and the proportion of “missingness” is unknown. We use auxiliary information such as the distribution of covariates or their moments in random sampling data in order to correct the bias. Moreover, with additional assumptions, we can correct the bias even if we have only the moment information of covariates. The main contribution of this paper is the development of a doubly robust-type estimator for biased sampling data. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. We prove the consistency and semi-parametric efficiency of the doubly robust estimator. Both the simulation and empirical application results demonstrate that the proposed estimation method is more robust than existing methods.

• Access to Document (DOI)

小学1年生から中学3年生を対象とした学力テストの垂直尺度化

山口一大・敷島千鶴・星野崇宏・繁桝算男・赤林英夫

心理学研究 印刷中 2019

Single Work, Accepted

Semiparametric Bayesian Multiple Imputation for Regression Models with Missing Mixed Continuous-Discrete Covariates

Ryo Kato and Takahiro Hoshino

Annals of the Institute of Statistical Mathematics (Annals of the Institute of Statistical Mathematics)  in press 2019

Research paper (scientific journal), Single Work, Accepted,  ISSN  00203157

　View Summary

© 2019, The Institute of Statistical Mathematics, Tokyo. Issues regarding missing data are critical in observational and experimental research. Recently, for datasets with mixed continuous–discrete variables, multiple imputation by chained equation (MICE) has been widely used, although MICE may yield severely biased estimates. We propose a new semiparametric Bayes multiple imputation approach that can deal with continuous and discrete variables. This enables us to overcome the shortcomings of MICE; they must satisfy strong conditions (known as compatibility) to guarantee obtained estimators are consistent. Our simulation studies show the coverage probability of 95% interval calculated using MICE can be less than 1%, while the MSE of the proposed can be less than one-fiftieth. We applied our method to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and the results are consistent with those of the previous works that used panel data other than ADNI database, whereas the existing methods, such as MICE, resulted in inconsistent results.

• Access to Document (DOI)