[1] Joshua D. Angrist. "Lifetime earnings and the vietnam era draft lotter: Evidence from social security administrative records". In: The American Economic Review 80.3 (1990), pp. 313-336.
[2] Chen Avin, Ilya Shpitser, and Judea Pearl. "Identifiability of path-specific effects". In: *IJCAI*. 2005.
[3] Vahid Balazadeh, Vasilis Syrgkanis, and Rahul G. Krishnan. "Partial identification of treatment effects with implicit generative models". In: NeurIPS. 2022.
[4] Elias Baireinboim et al. “On Pearl's hierarchy and the foundations of causal inference”. In: Probabilistic and Causal Inference: The Works of Judea Pearl (2022), pp. 507–556.
[5] Andrew Bennett, Nathan Kallus, and Tobias Schnabel. "Deep generalized method of moments for instrumental variable analysis". In: NeurIPS. 2019.
[6] Jeroen Berrevoets et al. "To Impute or not to Impute? Missing data in treatment effect estimation". In: AISTATS. 2023.
[7] Ioana Bica, James Jordon, and Mihaela van der Schaar. "Estimating the effects of continuous-valued interventions using generative adversarial networks". In: NeurIPS. 2020.
[8] Ioana Bica et al. "Estimating counterfactual treatment outcomes over time through adversarially balanced representations". In: ICLR. 2020.
[9] Stephan Bongers et al. "Foundations of structural causal models with cycles and latent variables". In: The Annals of Statistics 49.5 (2021), pp. 2885-2915.
[10] Matteo Bonvini et al. "Sensitivity analysis for marginal structural models". In: arXiv preprint arXiv:2210.04681 (2022).
[11] Kan Chen, Bingkai Wang, and Dylan S. Small. "A differential effect approach to partial identification of treatment effects". In: arXiv preprint arXiv:2303.06332 (2023).
[12] Victor Chernozhukov, Ivan Fernández-Val, and Blaise Melly. “Inference on counterfactual distributions”. In: Econometrica 81.6 (2013), pp. 2205–2268.
[13] Victor Chernozhukov et al. "Double/debiased machine learning for treatment and structural parameters". In: The Econometrics Journal 21.1 (2018), pp. C1-C68.
[14] James Cornfield et al. "Smoking and lung cancer: Recent evidence and a discussion of some questions". In: Journal of the National Cancer Institute 22.1 (1959), pp. 173-203.
[15] Juan D. Correa, Sanghack Lee, and Elias Bareinboim. "Nested counterfactual identification from arbitrary surrogate experiments". In: NeurIPS. 2021.
[16] Alicia Curth and Mihaela van der Schaar. "Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms". In: AISTATS. 2021.
[17] David L. Donoho and Peter J. Huber. "The notion of breakdown point". In: A Festschrift for Erich L. Lehmann (1983), pp. 157-184.
[18] Jacob Dorn and Kevin Guo. "Sharp sensitivity analysis for inverse propensity weighting via quantile balancing". In: Journal of the American Statistical Association (2022).
[19] Jacob Dorn, Kevin Guo, and Nathan Kallus. "Doubly-valid/ doubly-sharp sensitivity analysis for causal inference with unmeasured confounding". In: arXiv preprint arXiv:2112.11449 (2022).
[20] Guilherme Duarte et al. "An automated approach to causal inference in discrete settings". In: Journal of the American Statistical Association (2023).
[21] Conor Durkan et al. "Neural spline flows". In: NeurIPS. 2019.
[22] Helmut Farbmacher et al. "Causal mediation analysis with double machine learning". In: The Econometrics Journal 25.2 (2022), pp. 277-300.
[23] Dennis Frauen and Stefan Feuerriegel. "Estimating individual treatment effects under unobserved confounding using binary instruments". In: *ICLR*. 2023.
[24] Dennis Frauen et al. "Estimating average causal effects from patient trajectories". In: AAAI. 2023.