Abstract
One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect estimation often suffers from the impacts of confounding bias caused by unmeasured confounders that affect both the treatment and the outcome. The instrumental variable (IV) approach is a powerful way to eliminate the confounding bias from latent confounders. However, the existing IV-based estimators require a nominated IV, and for a conditional IV (CIV) the corresponding conditioning set too, for causal effect estimation. This limits the application of IV-based estimators. In this paper, by leveraging the advantage of disentangled representation learning, we propose a novel method, named DVAE.CIV, for learning and disentangling the representations of CIV and the representations of its conditioning set for causal effect estimations from data with latent confounders. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed DVAE.CIV method against the existing causal effect estimators.
D. Cheng and Z. Xu—These authors contributed equally.
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References
Abadie, A., Imbens, G.W.: Large sample properties of matching estimators for average treatment effects. Econometrica 74(1), 235–267 (2006)
Angrist, J.D., Imbens, G.W.: Two-stage least squares estimation of average causal effects in models with variable treatment intensity. J. Am. Stat. Assoc. 90(430), 431–442 (1995)
Angrist, J.D., Imbens, G.W., Rubin, D.B.: Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91(434), 444–455 (1996)
Athey, S., Tibshirani, J., Wager, S.: Generalized random forests. Ann. Stat. 47(2), 1148–1178 (2019)
Bingham, E., Chen, J.P., et al.: Pyro: deep universal probabilistic programming. J. Mach. Learn. Res. 20(1), 973–978 (2019)
Brito, C., Pearl, J.: Generalized instrumental variables. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 85–93 (2002)
Card, D.: Using geographic variation in college proximity to estimate the return to schooling (1993)
Cattaneo, M.D.: Efficient semiparametric estimation of multi-valued treatment effects under ignorability. J. Econometrics 155(2), 138–154 (2010)
Cheng, D., Li, J., Liu, L., Liu, J., Le, T.D.: Data-driven causal effect estimation based on graphical causal modelling: A survey. arXiv preprint arXiv:2208.09590 (2022)
Chernozhukov, V., et al.: Double/debiased machine learning for treatment and structural parameters. Econometrics J. 21(1), C1–C68 (2018)
Connors, A.F., Speroff, T., et al.: The effectiveness of right heart catheterization in the initial care of critically III patients. J. Am. Med. Assoc. 276(11), 889–897 (1996)
Foster, D.J., Syrgkanis, V.: Orthogonal statistical learning. arXiv preprint arXiv:1901.09036 (2019)
Guo, R., Cheng, L., Li, J., Hahn, P.R., Liu, H.: A survey of learning causality with data: problems and methods. ACM Comput. Surv. (CSUR) 53(4), 1–37 (2020)
Hartford, J., Lewis, G., et al.: Deep IV: A flexible approach for counterfactual prediction. In: International Conference on Machine Learning, pp. 1414–1423 (2017)
Hassanpour, N., Greiner, R.: Learning disentangled representations for counterfactual regression. In: International Conference on Learning Representations, pp. 1–11 (2019)
Hernán, M.A., Robins, J.M.: Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17(4), 360–372 (2006)
Hill, J.L.: Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20(1), 217–240 (2011)
Imbens, G.W.: Instrumental variables: an econometrician’s perspective. Stat. Sci. 29(3), 323–358 (2014)
Imbens, G.W., Rubin, D.B.: Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press, Cambridge (2015)
Kang, H., Zhang, A., Cai, T.T., Small, D.S.: Instrumental variables estimation with some invalid instruments and its application to mendelian randomization. J. Am. Stat. Assoc. 111(513), 132–144 (2016)
Khemakhem, I., Kingma, D., Monti, R., Hyvarinen, A.: Variational autoencoders and nonlinear ICA: a unifying framework. In: International Conference on Artificial Intelligence and Statistics, pp. 2207–2217. PMLR (2020)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)
Kuang, Z., Sala, F., et al.: Ivy: instrumental variable synthesis for causal inference. In: International Conference on Artificial Intelligence and Statistics, pp. 398–410 (2020)
LaLonde, R.J.: Evaluating the econometric evaluations of training programs with experimental data. Am. Econ. Rev. 76(4), 604–620 (1986)
Loh, W.W., Vansteelandt, S.: Confounder selection strategies targeting stable treatment effect estimators. Stat. Med. 40(3), 607–630 (2021)
Louizos, C., Shalit, U., Mooij, J.M., Sontag, D., Zemel, R., Welling, M.: Causal effect inference with deep latent-variable models. In: Advances in Neural Information Processing Systems, pp. 6446–6456 (2017)
Martens, E.P., Pestman, W.R., de Boer, A., Belitser, S.V., Klungel, O.H.: Instrumental variables: application and limitations. Epidemiology 17(3), pp. 260–267 (2006)
Martinussen, T., Nørbo Sørensen, D., Vansteelandt, S.: Instrumental variables estimation under a structural cox model. Biostatistics 20(1), 65–79 (2019)
Paszke, A., Gross, S., et al.: Pytorch: an imperative style, high-performance deep learning library. In: International Conference on Neural Information Processing Systems, pp. 8026–8037 (2019)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)
Sohn, K., Yan, X., Lee, H.: Learning structured output representation using deep conditional generative models. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. vol. 2, pp. 3483–3491 (2015)
Syrgkanis, V., Lei, V., et al.: Machine learning estimation of heterogeneous treatment effects with instruments. In: International Conference on Neural Information Processing Systems, pp. 15193–15202 (2019)
Verbeek, M.: A Guide to Modern Econometrics. Wiley, Hoboken (2008)
Wager, S., Athey, S.: Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113(523), 1228–1242 (2018)
Yuan, J., Wu, A., et al.: Auto IV: counterfactual prediction via automatic instrumental variable decomposition. ACM Trans. Knowl. Discov. Data 16(4), 1–20 (2022)
Van der Zander, B., Liśkiewicz, M., Textor, J.: Efficiently finding conditional instruments for causal inference, pp. 3243–3249 (2015)
Zhang, W., Liu, L., Li, J.: Treatment effect estimation with disentangled latent factors. In: The AAAI Conference on Artificial Intelligence, pp. 10923–10930 (2021)
Acknowledgments
This work has been supported by the Australian Research Council (grant number: DP200101210 and DP230101122).
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Cheng, D., Xu, Z., Li, J., Liu, L., Le, T.D., Liu, J. (2023). Learning Conditional Instrumental Variable Representation for Causal Effect Estimation. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_31
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