社会階層論 2026

東京大学大学院人文社会系研究科（S1S2・水2）

授業の目的

本授業の目的は，特に因果推論や機械学習に注目した実証的研究の方法についての議論を行いながら，近年の社会階層論に関する理論的，実証的，そして方法的展開を整理し第5世代の社会階層論について議論することである。

授業の概要と方法

本授業では，社会階層論をはじめとする社会学的研究において因果推論をどのように行うかを，主要文献の精読を通じて学ぶ。なぜ社会科学で因果推論が必要となるのかという問いから出発し，因果効果の定義とランダム化実験，観察研究における識別，因果グラフ（DAG）とコントロール変数の選択，処置効果の異質性，因果媒介分析，因果分解，因果機械学習といったトピックを順に取り上げる。

（社会学における）因果推論や機械学習に関する論文あるいは教科書の1〜2章あるいは論文を1〜2本読み，事前にコメントを提出する。当日はそのコメントを中心に議論を行う。経済学，政治学，疫学に関する論文についてもフォローする。第1回でリストを確認した上で，参加者の関心に応じて進度を調整することがある。

Note

日程・文献は変更されることがある。最新の開講情報はLectureのページを参照のこと。

スケジュール

第1回　4/8　ガイダンス

授業の概要，自己紹介，論文リストの確認。

第2回　4/15　なぜ因果推論か

第3回　4/22　因果効果の定義とランダム化実験

  • Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. — Chapter 1 “A Definition of Causal Effect” & Chapter 2 “Randomized Experiments”. https://miguelhernan.org/whatifbook

第4回　5/13　観察研究・効果修飾・交互作用

  • Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. — Chapter 3 “Observational Studies”, Chapter 4 “Effect Modification”, Chapter 5 “Interaction”. https://miguelhernan.org/whatifbook

5/20　休講（学会のため）

第5回　5/27　DAG入門：因果グラフの読み方

  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629

第6回　6/3　良いコントロール変数・悪いコントロール変数

第7回　6/10　処置効果の異質性

  • Brand, J. E., Xu, J., Koch, B., & Geraldo, P. (2021). Uncovering sociological effect heterogeneity using tree-based machine learning. Sociological Methodology, 51(2), 201–234. https://doi.org/10.1177/0081175021993503

第8回　6/17　因果媒介分析：応用研究者のための整理

  • Nguyen, T. Q., Schmid, I., & Stuart, E. A. (2021). Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn. Psychological Methods, 26(2), 255–271. https://doi.org/10.1037/met0000299
  • ［補足］Nguyen, T. Q., Schmid, I., Ogburn, E. L., & Stuart, E. A. (2022). Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes. Journal of Causal Inference, 10(1), 246–279. https://doi.org/10.1515/jci-2021-0049

第9回　6/24　因果分解による格差分析

第10回　7/1　サーベイ推論と因果グラフ

第11回　7/8　因果機械学習：演繹的・帰納的枠組み

  • Jeon, N., & Brand, J. E. (2026). Causal machine learning: A deductive–inductive framework for sociological research. Kölner Zeitschrift für Soziologie und Sozialpsychologie. https://doi.org/10.1007/s11577-026-01053-0

第12回　7/15　連続処置の因果推論：加法シフト・エスティマンド

  • Lundberg, I., & Brand, J. E. (2026). Causal inference with a continuous treatment: Addressing positivity constraints, nonlinearity, and effect heterogeneity. Sociological Methodology. https://doi.org/10.1177/00811750261459364

参考文献

教科書・包括書

  • Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.
  • Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Routledge. https://theeffectbook.net/
  • Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press.
  • Lieberson, S. (1985). Making It Count: The Improvement of Social Research and Theory. University of California Press.
  • Morgan, S. L. (Ed.). (2013). Handbook of Causal Analysis for Social Research. Springer.
  • Morgan, S. L., & Winship, C. (2014). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). Cambridge University Press.
  • Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley.
  • Pearl, J., & Mackenzie, D. (2020). The Book of Why: The New Science of Cause and Effect. Basic Books.
  • VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press.
  • 篠崎智大・萩原康博・田栗正隆・松山裕（2024）『疫学・臨床研究のための因果推論：Robinsのg-methodsによる現実問題へのアプローチ』朝倉書店.
  • 川口康平・澤田真行（2024）『因果推論の計量経済学』日本評論社.

DAG・因果グラフ

  • Brand, J. E., Zhou, X., & Xie, Y. (2023). Recent developments in causal inference and machine learning. Annual Review of Sociology, 49, 81–110.
  • Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of Causal Analysis for Social Research (pp. 245–273). Springer.
  • Elwert, F., & Winship, C. (2014). Endogenous selection bias: The problem of conditioning on a collider variable. Annual Review of Sociology, 40, 31–53. https://doi.org/10.1146/annurev-soc-071913-043455
  • Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37–48.
  • Westreich, D., & Greenland, S. (2013). The Table 2 fallacy: Presenting and interpreting confounder and modifier coefficients. American Journal of Epidemiology, 177(4), 292–298.

社会移動・移動効果モデル

  • Wei, L., & Xie, Y. (2025). Social mobility as causal intervention. Sociological Methods & Research.
  • Song, X., & Zhou, X. (2025). Is there a mobility effect? On methodological issues in the mobility contrast model. Sociological Methods & Research, 54(4), 1576–1593. https://doi.org/10.1177/00491241251347983

格差分析

  • Opacic, A., Wei, L., & Zhou, X. (2025). Disparity analysis: A tale of two approaches. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1093/jrsssa/qnaf008
  • Lundberg, I. (2022). The gap-closing estimand: A causal approach to study interventions that close disparities across social categories. Sociological Methods & Research. https://doi.org/10.1177/00491241211055769
  • Jackson, J. W. (2021). Meaningful causal decompositions in health equity research: Definition, identification, and estimation through a weighting framework. Epidemiology, 32(2), 282–290. https://doi.org/10.1097/EDE.0000000000001319
  • VanderWeele, T. J., & Robinson, W. R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology, 25(4), 473–484.

近隣効果・媒介分析の応用

  • Wodtke, G. T., Yildirim, U., Harding, D. J., & Elwert, F. (2023). Are neighborhood effects explained by differences in school quality? American Journal of Sociology, 128(5), 1472–1528. https://doi.org/10.1086/724279
  • Sharkey, P. (2008). The intergenerational transmission of context. American Journal of Sociology, 113(4), 931–969.
  • Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: Multigenerational neighborhood effects on cognitive ability. American Journal of Sociology, 116(6), 1934–1981. https://doi.org/10.1086/660009
  • Wodtke, G. T., Harding, D. J., & Elwert, F. (2011). Neighborhood effects in temporal perspective: The impact of long-term exposure to concentrated disadvantage on high school graduation. American Sociological Review, 76(5), 713–736.

Multiverse Analysis・頑健性

  • Engzell, P., & Mood, C. (2023). Understanding patterns and trends in income mobility through multiverse analysis. American Sociological Review, 88(4), 600–626.
  • Young, C., & Holsteen, K. (2017). Model uncertainty and robustness: A computational framework for multimodel analysis. Sociological Methods & Research, 46(1), 3–40.
  • Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712.

教育効果・処置効果の異質性の応用

  • Brand, J. E., & Xie, Y. (2010). Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. American Sociological Review, 75(2), 273–302.
  • Brand, J. E., & Davis, D. (2011). The impact of college education on fertility: Evidence for heterogeneous effects. Demography, 48(3), 863–887. https://doi.org/10.1007/s13524-011-0034-3
  • Xie, Y., Brand, J. E., & Jann, B. (2012). Estimating heterogeneous treatment effects with observational data. Sociological Methodology, 42(1), 314–347.
  • Torche, F. (2011). Is a college degree still the great equalizer? Intergenerational mobility across levels of schooling in the United States. American Journal of Sociology, 117(3), 763–807.
  • Zhou, X. (2022). Equalization or selection? Reassessing the “meritocratic power” of a college degree in intergenerational income mobility. American Sociological Review, 87(2), 322–355.

因果推論の広いレビュー

  • Imbens, G. W. (2024). Causal inference in the social sciences. Annual Review of Statistics and Its Application, 11, 18.1–18.30.
  • Brand, J. E., Zhou, X., & Xie, Y. (2023). Recent developments in causal inference and machine learning. Annual Review of Sociology, 49, 81–110.

深層学習・機械学習による因果推論

  • Koch, B. J., Sainburg, T., Geraldo Bastías, P., Jiang, S., Sun, Y., & Foster, J. G. (2024). A primer on deep learning for causal inference. Sociological Methods & Research, 54(2), 397–447.
  • Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. PNAS, 113(27), 7353–7360.
  • Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.
  • Chernozhukov, V., et al. (2018). Double/debiased machine learning for treatment and structural parameters. Econometrics Journal, 21(1), C1–C68.

ハザード比・生存分析と因果推論

  • Hernán, M. A. (2010). The hazards of hazard ratios. Epidemiology, 21(1), 13–15.
  • Stensrud, M. J., & Hernán, M. A. (2020). Why test for proportional hazards? JAMA, 323(14), 1401–1402.

今年度の論文選定で検討した候補（参考）

本年度シラバスを編成する際に，以下の論文を主要課題候補として検討した。本年度は採用しなかったが，関連分野の重要論文として参照可能。

  • Wei, L., & Xie, Y. (2025). Social mobility as causal intervention. Sociological Methods & Research.
  • Song, X., & Zhou, X. (2025). Is there a mobility effect? Sociological Methods & Research, 54(4), 1576–1593.
  • Wodtke, G. T., Yildirim, U., Harding, D. J., & Elwert, F. (2023). Are neighborhood effects explained by differences in school quality? American Journal of Sociology, 128(5), 1472–1528.
  • Opacic, A., Wei, L., & Zhou, X. (2025). Disparity analysis: A tale of two approaches. Journal of the Royal Statistical Society Series A.
  • Engzell, P., & Mood, C. (2023). Understanding patterns and trends in income mobility through multiverse analysis. American Sociological Review, 88(4), 600–626.
  • Brand, J. E., & Davis, D. (2011). The impact of college education on fertility. Demography, 48(3), 863–887.
  • Lundberg, I. (2022). The gap-closing estimand. Sociological Methods & Research.
  • Imbens, G. W. (2024). Causal inference in the social sciences. Annual Review of Statistics and Its Application, 11.
  • Koch, B. J., et al. (2024). A primer on deep learning for causal inference. Sociological Methods & Research, 54(2), 397–447.
  • Felton, C., & Stewart, B. M. (2026). Handle with care: A sociologist’s guide to causal inference with instrumental variables. Sociological Methods & Research, 55(1), 3–50. https://doi.org/10.1177/00491241241235900

他については授業中に適宜紹介する。

過去の使用テキスト

2024年度

基礎は林岳彦テキスト，後半は最新方法論の論文読み。

  • 4/10　ガイダンス
  • 4/17　林岳彦（2024）『はじめての統計的因果推論』岩波書店．1–2章
  • 4/24　同書 3–4章
  • 5/1　同書 5–6章
  • 5/8　同書 6–7章
  • 5/22　同書 8–9章
  • 5/29　g-computation から TMLE へ
    • Snowden, J. M., Rose, S., & Mortimer, K. M. (2011). Implementation of g-computation on a simulated data set. American Journal of Epidemiology, 173(7), 731–738.
    • Vansteelandt, S., & Keiding, N. (2011). Invited commentary: G-computation—Lost in translation? American Journal of Epidemiology, 173(7), 739–742.
    • Rose, S., Snowden, J. M., & Mortimer, K. M. (2011). Rose et al. respond to “G-computation and standardization in epidemiology.” American Journal of Epidemiology, 173(7), 743–744.
  • 6/5　SuperLearner
    • Naimi, A. I., & Balzer, L. B. (2018). Stacked generalization: An introduction to super learning. European Journal of Epidemiology, 33(5), 459–464.
    • Polley, E., & van der Laan, M. (2010). Super learner in prediction. UC Berkeley Working Paper.
  • 6/12　TMLE
    • Schuler, M. S., & Rose, S. (2017). Targeted maximum likelihood estimation for causal inference in observational studies. American Journal of Epidemiology, 185(1), 65–73.
  • 6/19　Causal inference with text
    • Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). How to make causal inferences using texts. Science Advances, 8(42), eabg2652.
  • 6/26　Multiple versions of treatment
    • VanderWeele, T. J., & Hernán, M. A. (2013). Causal inference under multiple versions of treatment. Journal of Causal Inference, 1(1), 1–20.
  • 7/3　Machine learning
    • Verhagen, M. D. (2024). Incorporating machine learning into sociological model-building. Sociological Methodology.
  • 7/10　Prediction
    • Salganik, M. J., Lundberg, I., Kindel, A. T., et al. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. PNAS, 117(15), 8398–8403.

2025年度　社会階層論（S1S2）

  • Morgan, S. L., & Winship, C.（落海浩・松林哲也訳）（2025）『反事実と因果推論』ミネルヴァ書房． （原著：Morgan, S. L., & Winship, C. (2014). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). Cambridge University Press.）

2025年度　計量社会科学（A1A2）

前半（第1–6回）：Pearl, J., & Mackenzie, D.（夏目大訳）（2022）『因果推論の科学：「なぜ？」の問いをどう答えるか』文藝春秋．

後半（論文読み）：

  • 11/19　Smith, M. J., Mansournia, M. A., Maringe, C., Zivich, P. N., Cole, S. R., Leyrat, C., Belot, A., Rachet, B., & Luque-Fernandez, M. A. (2022). Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial. Statistics in Medicine, 41(2), 407–432. https://doi.org/10.1002/sim.9234
  • 12/3　Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532–565. https://doi.org/10.1177/00031224211004187
  • 1/7　Opacic, A., Wei, L., & Zhou, X. (2025). Disparity analysis: A tale of two approaches. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1093/jrsssa/qnaf008
  • 1/14　Brand, J. E., Zhou, X., & Xie, Y. (2023). Recent developments in causal inference and machine learning. Annual Review of Sociology, 49(1), 81–110. https://doi.org/10.1146/annurev-soc-030420-015345