Kallus, N. (2020). DeepMatch: Balancing deep covariate representations for causal inference using adversarial training. In I. H. Daumé, & A. Singh (Eds.), Proceedings of the 37th international conference on machine learning. In Proceedings of Machine Learning Research: vol. 119 (pp. 5067–5077). PMLR

Using adversarial deep learning approaches to get a better correction for causal inference from observational data.

"Causal Deep Learning" Authors:Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

Very general and ambitious approach for representing the full continuous conceptual spectrum of Pearl's Causal Ladder, and ability to model and learning parts of this from Data.

Discussed by Prof. van der Shaar at ICML2023 workshop on Counterfactuals.

Question: how does this notion of hypothesis space relate to causal inference and reasoning?

[ Bengio, The Consciousness Prior, Arxiv, 2018]

Very general and ambitious approach for representing the full continuous conceptual spectrum of Pearl's Causal Ladder, and ability to model and learning parts of this from Data.

Discussed by Prof. van der Shaar at ICML2023 workshop on Counterfactuals.

(Cousineau,Verter, Murphy and Pineau, 2023) " Estimating causal effects with optimization-based methods: A review and empirical comparison"

To avoid making false conclusions, studies must identify all the sources of variation. Is this is even possible in most caes?

Matching approach to dealing with sampling bias. Basically use some intrinsic, or other, metric about the situations to cluster them so that "similar" situations will be dealt with similiarly. Then analysis is carried out on those clusters. Number of clusters has to be defined, some method, like k-means, if often used. Depends a lot on the similarity metric, the clustering approach, other assumptions

Terwiesch, 2022 - "A review of Empircal Operations Managment over the Last Two Decades" Listed as an important review of methods for addressing biases in Operations management by explicitly addressing causality.

Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, and Mark Crowley Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting In International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA. Jul, 2023.

"Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning" Yuejiang Liu1, 2,* YUEJIANG.LIU@EPFL.CH Alexandre Alahi2 ALEXANDRE.ALAHI@EPFL.CH Chris Russell1 CMRUSS@AMAZON.DE Max Horn1 HORNMAX@AMAZON.DE Dominik Zietlow1 ZIETLD@AMAZON.DE Bernhard Sch ̈olkopf1, 3 BS@TUEBINGEN.MPG.DE Francesco Locatello1 LOCATELF@AMAZON.DE

ReconfigBehSci [@SciBeh]. ‘RT @CAUSALab: Interested in #causalinference? Learn from Top Experts in the Field. Summer Courses Offered at the Harvard T.H. Chan Schoo…’. Tweet. Twitter, 20 December 2021. https://twitter.com/SciBeh/status/1483138177837715464.

Nast, C. (2022, January 15). Do the Omicron Numbers Mean What We Think They Mean? The New Yorker. https://www.newyorker.com/magazine/2022/01/24/do-the-omicron-numbers-mean-what-we-think-they-mean

Tran, V.-T., Perrodeau, E., Saldanha, J., Pane, I., & Ravaud, P. (2021). Efficacy of COVID-19 Vaccination on the Symptoms of Patients With Long COVID: A Target Trial Emulation Using Data From the ComPaRe e-Cohort in France (SSRN Scholarly Paper ID 3932953). Social Science Research Network. https://papers.ssrn.com/abstract=3932953

Are 7 French fries too many?. A causal inference explainer | by Ellie Murray | Medium. (n.d.). Retrieved August 22, 2021, from https://medium.com/@EpiEllie/are-7-french-fries-too-many-d6226e78dc1f

Rohrer, J. M., Schmukle, S., & McElreath, R. (2021). The Only Thing That Can Stop Bad Causal Inference Is Good Causal Inference. PsyArXiv. https://doi.org/10.31234/osf.io/mz5jx

Blakely, Tony, John Lynch, Koen Simons, Rebecca Bentley, and Sherri Rose. ‘Reflection on Modern Methods: When Worlds Collide—Prediction, Machine Learning and Causal Inference’. International Journal of Epidemiology 49, no. 6 (1 December 2020): 2058–64. https://doi.org/10.1093/ije/dyz132.

The Alan Turing Institute: Causal Inference, Causal Decision Making Under Uncertainty | CogX 2020. (2020, June 25). https://www.youtube.com/watch?v=JAGRHbDLvUs

Leininger, A., & Schaub, M. (2020). Voting at the dawn of a global pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a32r7

Causal inference isn’t what you think it is. (n.d.). LSHTM. Retrieved June 26, 2020, from https://www.lshtm.ac.uk/newsevents/events/causal-inference-isnt-what-you-think-it

Marshall, B. D. L., & Galea, S. (2015). Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology. American Journal of Epidemiology, 181(2), 92–99. https://doi.org/10.1093/aje/kwu274