Three critical issues for causal inference that often occur in modern, complicated experiments are interference, treatment nonadherence, and missing outcomes. A great deal of research efforts has been dedicated to developing causal inferential methodologies that address these issues separately. However, methodologies that can address these issues simultaneously are lacking. We propose a Bayesian causal inference methodology to address this gap. Our methodology extends existing causal frameworks and methods, specifically, two-staged randomized experiments and the principal stratification framework. In contrast to existing methods that invoke strong structural assumptions to identify principal causal effects, our Bayesian approach uses flexible distributional models that can accommodate the complexities of interference and missing outcomes, and that ensure that principal causal effects are weakly identifiable. We illustrate our methodology via simulation studies and a re-analysis of real-life data from an evaluation of India's National Health Insurance Program. Our methodology enables us to identify new active causal effects that were not identified in past analyses. Ultimately, our simulation studies and case study demonstrate how our methodology can yield more informative analyses in modern experiments with interference, treatment nonadherence, missing outcomes, and complicated outcome generation mechanisms.
Estimating the duration of user behavior is a central concern for most internet companies. Survival analysis is a promising method for analyzing the expected duration of events and usually assumes the same survival function for all subjects and the event will occur in the long run. However, such assumptions are inappropriate when the users behave differently or some events never occur for some users, i.e., the conversion period on web services of the light users with no intention of behaving actively on the service. Especially, if the proportion of inactive users is high, this assumption can lead to undesirable results. To address these challenges, this paper proposes a mixture model that separately addresses active and inactive individuals with a latent variable. First, we define this specific problem setting and show the limitations of conventional survival analysis in addressing this problem. We demonstrate how naturally our Bernoulli-Weibull model can accommodate the challenge. The proposed model was extended further to a Bayesian hierarchical model to incorporate each subject's parameter, offering substantial improvements over conventional, non-hierarchical models in terms of WAIC and WBIC. Second, an experiment and extensive analysis were conducted using real-world data from the Japanese job search website, CareerTrek, offered by BizReach, Inc. In the analysis, some research questions are raised, such as the difference in activation rate and conversion rate between user categories, and how instantaneously the rate of event occurrence changes as time passes. Quantitative answers and interpretations are assigned to them. Furthermore, the model is inferred in a Bayesian manner, which enables us to represent the uncertainty with a credible interval of the parameters and predictive quantities.
Job interviews are a fundamental activity for most corporations to acquire potential candidates, and for job seekers to get well-rewarded and fulfilling career opportunities. In many cases, interviews are conducted in multiple processes such as telephone interviews and several face-to-face interviews. At each stage, candidates are evaluated in various aspects. Among them, grade evaluation, such as a rating on a 1-4 scale, might be used as a reasonable method to evaluate candidates. However, because each evaluation is based on a subjective judgment of interviewers, the aggregated evaluations can be biased because the difference in toughness of interviewers is not examined. Additionally, it is noteworthy that the toughness of interviewers might vary depending on the interview round. As described herein, we propose an analytical framework of simultaneous estimation for both the true potential of candidates and toughness of interviewers' judgment considering job interview rounds, with algorithms to extract unseen knowledge of the true potential of candidates and toughness of interviewers as latent variables through analyzing grade data of job interviews. We apply a Bayesian Hierarchical Ordered Probit Model to the grade data from HRMOS, a cloud-based Applicant Tracking System (ATS) operated by BizReach, Inc., an IT start-up particularly addressing human-resource needs in Japan. Our model successfully quantifies the candidate potential and the interviewers' toughness. An interpretation and applications of the model are given along with a discussion of its place within hiring processes in real-world settings. The parameters are estimated by Markov Chain Monte Carlo (MCMC). A discussion of uncertainty, which is given by the posterior distribution of the parameters, is also provided along with the analysis.
サブプライムローン問題に端を発した世界的な金融危機の影響 を受け,我が国の雇用情勢も急激に悪化した.平成 24 年度末 においても地域によって差異はあるものの,依然厳しい状況が 続いている. 本論文において,平成 7 年,12 年及び 17 年地域間産業連関表 と整合した地域雇用表を推計した.その際,従業者数に関する 一次統計に加え,賃金額に関する一次統計も用いている.ま た,推計した地域雇用表を用いて就業構造の変化とその要因分 解を行った.その結果,地域ごとに異なる就業構造等が確認さ れた. 1.はじめに リーマン・ブラザーズ破綻に象徴される世界 的な金融危機によって,雇用情勢は急激に悪化 した.その後,緩やかな回復基調にあるもの の,平成 25 年 3 月現在,宮城県(1.29 倍)等 8 都県を除く道府県において有効求人倍率は 1 倍を割った状態が続いている. また,雇用政策も 2000 年を機に大きく変化 することとなった.それまでの雇用政策は,公 共事業等国主導のものであった.しかし,地方 分権の推進を図るための関係法律の整備等に関 する法律の施行を契機に,雇用政策もまた地方 主導のものへと変わっていった. 地域雇用の分析において,産業連関表は有効 なツールであると考えられる.しかし,経済産 業省が作成している地域間産業連関表(以下, 「地域間表」という. )において,雇用表は公表 されていない.産業連関表は都道府県等によっ ても作成されているが,雇用表はすべての都道 府県が公表しているわけではない.また,山 田・朝日(1999a)によると,都道府県ごとに 雇用表の作成方法には違いがあることが示され 88 大西 雄基 埼玉大学経済科学研究科博士後期課程 〒338-8570 さいたま市桜区下大久保 255 平成 7─ 12─ 17 年接続地域間表の推計方法 は以下のとおりである.なお,地域区分は表 1 のとおりである. ⑴ 部門統合 各年の地域間表において部門の概念・定義・ 範囲等が異なるため,表 2 の 26 部門へと統合 した. ⑵ 名目表の推計 上記⑴で統合した表の地域計を平成 7─ 12─ 17 年接続産業連関表(以下, 「接 続 表」と い う. )の名目表と一致させた 4) .このとき,部 門間の取引額ごとに 接続表の名目表における取引額 地域間表の地域計における取引額 を乗じているため,各地域・部門の列和と行和 90
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