 Oct 2023

proceedings.mlr.press proceedings.mlr.press

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.


arxiv.org arxiv.org

"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.
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www.nature.com www.nature.com

Performing optimization in the latent space can more flexibly model underlying data distributions than mechanistic approaches in the original hypothesis space. However, extrapolative prediction in sparsely explored regions of the hypothesis space can be poor. In many scientific disciplines, hypothesis spaces can be vastly larger than what can be examined through experimentation. For instance, it is estimated that there are approximately 1060 molecules, whereas even the largest chemical libraries contain fewer than 1010 molecules12,159. Therefore, there is a pressing need for methods to efficiently search through and identify highquality candidate solutions in these largely unexplored regions.
Question: how does this notion of hypothesis space relate to causal inference and reasoning?


arxiv.org arxiv.org

[ Bengio, The Consciousness Prior, Arxiv, 2018]
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arxiv.org arxiv.org

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.
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arxiv.org arxiv.org

(Cousineau,Verter, Murphy and Pineau, 2023) " Estimating causal effects with optimizationbased methods: A review and empirical comparison"
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oid.wharton.upenn.edu oid.wharton.upenn.edu

To avoid such bias, a fundamental aspect in the research design of studies of causalinference is the identification strategy: a clear definition of the sources of variation in the datathat can be used to estimate the causal effect of interest.
To avoid making false conclusions, studies must identify all the sources of variation. Is this is even possible in most caes?

Matching: This approach seeks to replicate a balanced experimental design usingobservational data by finding close matches between pairs or groups of units andseparating out the ones that received a specified treatment from those that did not, thusdefining the control groups.
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 kmeans, 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.


openreview.net openreview.net

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


arxiv.org arxiv.org

"Causal Triplet: An Open Challenge for Interventioncentric 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

 Apr 2022

twitter.com twitter.com

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.

 Jan 2022

www.newyorker.com www.newyorker.com

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/dotheomicronnumbersmeanwhatwethinktheymean

 Oct 2021

papers.ssrn.com papers.ssrn.com

Tran, V.T., Perrodeau, E., Saldanha, J., Pane, I., & Ravaud, P. (2021). Efficacy of COVID19 Vaccination on the Symptoms of Patients With Long COVID: A Target Trial Emulation Using Data From the ComPaRe eCohort in France (SSRN Scholarly Paper ID 3932953). Social Science Research Network. https://papers.ssrn.com/abstract=3932953

 Aug 2021

medium.com medium.com

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/are7frenchfriestoomanyd6226e78dc1f

 May 2021

psyarxiv.com psyarxiv.com

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

 Mar 2021

academic.oup.com academic.oup.com

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.

 Aug 2020

www.youtube.com www.youtube.com

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

 Jul 2020


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

 Jun 2020

www.lshtm.ac.uk www.lshtm.ac.uk

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


academic.oup.com academic.oup.com

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