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ADR: An Adversarial Approach to Learn Decomposed Representations for Causal Inference

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 14942))

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Abstract

Estimating the individual treatment effect (ITE) from observational data is an important issue both theoretically and practically. While including all the pre-treatment covariates is the common practice for the inclusion of all possible confounders, it may aggravate the issue of data imbalance. In this paper, we theoretically show that including extra information would increase the variance lower bound. Based on the causal graph, we decompose the covariates into three components, namely instrumental variables (I), confounders (C), and adjustment variables (A). BothC andA should be included for the ITE estimation, whileI should be avoided since it would aggravate the imbalance issue and contains no extra information for the ITE estimation. To facilitate the decomposed representation learning, we derive the probabilistic conditions for\(\{I, C, A\}\) from the graphical definitions, and theoretically show that such decomposition can be learned in an adversarial manner. Under the guidance of such theoretical justification, we propose the ADR algorithm, an adversarial learning approach to learn the decomposed representations and simultaneously estimate the treatment effect. The proposed algorithm can be applied to both categorical and numerical treatments and the effectiveness is assured by both theoretical analyses and empirical results. Experimental results on both synthetic and real data show that the ADR Algorithm is advantageous compared to the state-of-the-art methods. The theoretical analyses also provide a path to further explore the issue of decomposed representation learning for causal inference.

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References

  1. Jaskowski, M., Jaroszewicz, S.: Uplift modeling for clinical trial data. In: ICML Workshop on Clinical Data Analysis, vol. 46, pp. 79–95 (2012)

    Google Scholar 

  2. Alaa, A.M., Van Der Schaar, M.: Bayesian inference of individualized treatment effects using multi-task gaussian processes. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  3. Alaa, A.M., Weisz, M., Van Der Schaar, M.: Deep counterfactual networks with propensity-dropout. arXiv preprintarXiv:1706.05966 (2017)

  4. Sato, M., Singh, J., Takemori, S., Sonoda, T., Zhang, Q., Ohkuma, T.: Uplift-based evaluation and optimization of recommenders. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 296–304 (2019)

    Google Scholar 

  5. Wan, S., et al.: GCF: generalized causal forest for heterogeneous treatment effect estimation in online marketplace. arXiv preprintarXiv:2203.10975 (2022)

  6. Guelman, L., Guillén, M., Pérez-Marín, A.M.: A decision support framework to implement optimal personalized marketing interventions. Dec. Support Syst.72, 24–32 (2015)

    Google Scholar 

  7. Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol.66(5), 688 (1974)

    Article  Google Scholar 

  8. Splawa-Neyman, J., Dabrowska, D.M., Speed, T.P.: On the application of probability theory to agricultural experiments. essay on principles. Section 9. Stat. Sci. 465–472 (1990)

    Google Scholar 

  9. Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  10. Pearl, J.: Causal inference in statistics: an overview. Stat. Surv.3, 96–146 (2009)

    Article MathSciNet  Google Scholar 

  11. Künzel, S.R., Sekhon, J.S., Bickel, P.J., Yu, B.: Metalearners for estimating heterogeneous treatment effects using machine learning. Proc. Natl. Acad. Sci.116(10), 4156–4165 (2019)

    Article  Google Scholar 

  12. Imbens, G.W., Rubin, D.B.: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press, Cambridge (2015)

    Book  Google Scholar 

  13. Zhong, K., et al.: DESCN: deep entire space cross networks for individual treatment effect estimation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4612–4620 (2022)

    Google Scholar 

  14. Rubin, D.B.: For objective causal inference, design trumps analysis (2008)

    Google Scholar 

  15. Rubin, D.B.: Author’s reply (to judea pearl’s and arvid sjölander’s letters to the editor). Stat. Med.28, 1420–1423 (2009)

    Article MathSciNet  Google Scholar 

  16. Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. In: International Conference on Machine Learning, pp. 3076–3085. PMLR (2017)

    Google Scholar 

  17. Johansson, F., Shalit, U., Sontag, F.: Learning representations for counterfactual inference. In: International Conference on Machine Learning, pp. 3020–3029. PMLR (2016)

    Google Scholar 

  18. Johansson, F.D., Shalit, U., Kallus, N., Sontag, D.: Generalization bounds and representation learning for estimation of potential outcomes and causal effects. J. Mach. Learn. Res.23(1), 7489–7538 (2022)

    MathSciNet  Google Scholar 

  19. Hassanpour, N., Greiner, R.: Learning disentangled representations for counterfactual regression. In: International Conference on Learning Representations (2020)

    Google Scholar 

  20. Zhang, W., Liu, L., Li, J.: Treatment effect estimation with disentangled latent factors. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 10923–10930 (2021)

    Google Scholar 

  21. Anpeng, W., Yuan, J., Kuang, K., Li, B., Runze, W., Zhu, Q., Zhuang, Y., Fei, W.: Learning decomposed representations for treatment effect estimation. IEEE Trans. Knowl. Data Eng.35(5), 4989–5001 (2022)

    Google Scholar 

  22. Chauhan, V.K., Molaei, S., Tania, M.H., Thakur, A., Zhu, T., Clifton, D.A.: Adversarial de-confounding in individualised treatment effects estimation. In: International Conference on Artificial Intelligence and Statistics, pp. 837–849. PMLR (2023)

    Google Scholar 

  23. Hassanpour , N., Greiner, N.: Counterfactual regression with importance sampling weights. In: Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19 (2019)

    Google Scholar 

  24. Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stati. Assoc.100(469), 322–331 (2005)

    Article MathSciNet  Google Scholar 

  25. Rao, C.R., et al.: Information and the accuracy attainable in the estimation of statistical parameters. Breakthroughs in Statistics, pp. 235–247 (1992)

    Google Scholar 

  26. Cramér, H.: Mathematical Methods of Statistics, vol. 26. Princeton University Press (1999)

    Google Scholar 

  27. Hahn, J.: On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica, 315–331 (1998)

    Google Scholar 

  28. Goodfellow, I., et al.: Generative adversarial nets. In: Neural Information Processing Systems (2014)

    Google Scholar 

  29. Kuang, K., Cui, P., Li, B., Jiang, M., Yang, S., Wang, F.: Treatment effect estimation with data-driven variable decomposition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  30. Kuang, K., et al.: Data-driven variable decomposition for treatment effect estimation. IEEE Trans. Knowl. Data Eng.34(5), 2120–2134 (2020)

    Article  Google Scholar 

  31. Zhang, W., Li, J., Liu, L.: A unified survey of treatment effect heterogeneity modelling and uplift modelling. ACM Comput. Surv. (CSUR)54(8), 1–36 (2021)

    Google Scholar 

  32. Olaya, D., Coussement, K., Verbeke, W.: A survey and benchmarking study of multitreatment uplift modeling. Data Min. Knowl. Disc.34, 273–308 (2020)

    Article MathSciNet  Google Scholar 

  33. Yao, L., Li, S., Li, Y., Huai, M., Gao, J., Zhang, A.: Representation learning for treatment effect estimation from observational data. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  34. Hill, J.L.: Bayesian nonparametric modeling for causal inference. J. Comput. Graphical Stat.20(1), 217–240 (2011)

    Article MathSciNet  Google Scholar 

  35. Dorie, V.: NPCI: non-parametrics for causal inference (2016).https://github.com/vdorie/npci

Download references

Author information

Authors and Affiliations

  1. JD Technology, Beijing, China

    Xiangyu Zheng, Guogang Tian, Sen Wang & Zhixiang Huang

Authors
  1. Xiangyu Zheng

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  2. Guogang Tian

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  3. Sen Wang

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  4. Zhixiang Huang

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Corresponding author

Correspondence toXiangyu Zheng.

Editor information

Editors and Affiliations

  1. LTCI, Télécom Paris, Palaiseau Cedex, France

    Albert Bifet

  2. KU Leuven, Leuven, Belgium

    Jesse Davis

  3. Faculty of Informatics, Vytautas Magnus University, Akademija, Lithuania

    Tomas Krilavičius

  4. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Meelis Kull

  5. Department of Computer Science, Bundeswehr University Munich, Munich, Germany

    Eirini Ntoutsi

  6. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Indrė Žliobaitė

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zheng, X., Tian, G., Wang, S., Huang, Z. (2024). ADR: An Adversarial Approach to Learn Decomposed Representations for Causal Inference. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14942. Springer, Cham. https://doi.org/10.1007/978-3-031-70344-7_16

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