Causality reading group

The focus of this causality reading group is on mathematical theory for causal inference, with some meetings dedicated to methodological developments. The reading group is organized by Aad van der Vaart and me. Meetings take place on Mondays, 16.00-17.30, and are open to all. For the time being, the meetings will take place online. If you’d like to be added to the mailing list, please send me an e-mail.

If you’re interested in causal inference, the following online seminar may also be of interest to you: here.

The schedule (list of papers to consider for future discussion below):

Date	Location	Topic	Materials	Speaker
June 14, 2021, 16.00-17.30	Online	Ilse, Forré, Welling and Mooij (2021+). Efficient causal inference from combined observational and interventional data through causal reductions.		Joris Mooij
May 10, 2021, 16.00-17.30	Online	Optimal adjustment sets.		Andrea Rotnitzky
April 12, 2021, 16.00-17.30	Online	Henckel, Perkovic and Maathuis (2019+). Graphical criteria for efficient total effect estimation via adjustment in causal linear models.		Leonard Henckel
March 15, 2021, 16.00-17.30	Online	Bucur, Claassen and Heskes (2019). Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach.		Gabriel Bucur
February 22, 2021, 17.00-18.00	Online	Lei and Candès (2020+). Conformal inference of counterfactuals and individual treatment effects.		Lihua Lei
2020
November 23, 2020	Online	Bayesian nonparametric modeling, based on: Hill (2011). Bayesian nonparametric modeling for causal inference. Hahn, Murray, Carvalho (2020). Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects. Ray, Van der Vaart (2020+). Semiparametric Bayesian causal inference. Ray, Szabo (2019). Debiased Bayesian inference for average treatment effects.		Aad van der Vaart
November 2, 2020	Online	Young, Stensrud, Tchetgen Tchetgen, Hernán (2020). A causal framework for classical statistical estimands in failure time settings with competing events.	(e-mail Stéphanie or Richard for the slides)	Richard Post
October 12, 2020	Online	Goetghebeur, Le Cessie, De Stavola, Moodie, Waernbaum (2019+). Formulating causal questions and principled statistical answers.		Saskia le Cessie
September 28, 2020	Online	Van Geloven, Swanson, Ramspek, Luijken, Van Diepen, Morris, Groenwold, Van Houwelingen, Putter, Le Cessie (2020). Prediction meets causal inference: the role of treatment in clinical prediction models.		Nan van Geloven
March 9, 2020	Snellius, room 176	Robins, Rotnitzky (1995). Semiparametric efficiency in multivariate regression models with missing data.		Lasse Vuursteen
February 24, 2020	LUMC building 2, T-00-022	Diemer, Labrecque, Tiemeier, Swanson (2020). Application of the instrumental inequalities to a Mendelian randomization study with multiple proposed instruments.	Background on Mendelian randomization: video & article	Sonja Swanson, Lizzie Diemer
February 10, 2020	LUMC building 2, T-00-022	Toh and Hernán (2008). Causal inference from longitudinal studies with baseline randomization.		Bart Eggen
2019
December 9, 2019	LUMC building 2, T-00-022	Abadie and Imbens (2005). Large sample properties of matching estimators for average treatment effects.		Aad van der Vaart
November 25, 2019	LUMC building 3, V-04-018/022	Joint Causal Inference from Multiple Contexts		Joris Mooij
November 4, 2019	LUMC building 3, V-02-018/022	Hernán (2010). The hazard of hazard ratios. Aalen, Cook and Røysland (2015). Does Cox analysis of a randomized survival study yield a causal treatment effect?	slides	Hein Putter
October 14, 2019	LUMC building 3, V-04-052/056	Caliendo and Kopeinig (2005). Some practical guidance for the implementation of propensity score matching.	slides 1 slides 2 notes Rubin	Stéphanie van der Pas
May 27, 2019	LUMC building 3, V-03-036	Pearl, Chapters 9 and 10.	slides	Amine Hadji
May 13, 2019	LUMC building 2, T-00-022	Pearl, Chapter 8.		Richard Gill
April 8, 2019	LUMC building 3, V-02-018/022	Peters et al, Chapter 9.	slides	Stefan Franssen
March 25, 2019	LUMC building 3, V-02-034	Peters et al, Chapters 4 and 7.	slides	Richard Post
March 11, 2019	LUMC building 3, V-02-026/028	Peters et al, Chapter 6.	slides	Richard Gill
February 25, 2019	LUMC building 2, T-00-022	Peters et al, Chapters 1-3.	slides	Stéphanie van der Pas
2018
December 17, 2018	LUMC building 3, V-03-044	Hernán & Robins, Chapters 15-16.	notes	Svetlana Belitser
December 10, 2018	–	Extra materials: Note on Inverse Weighing and Double Robustness.	note	Aad van der Vaart
December 3, 2018	LUMC building 2, T-00-022	Hernán & Robins, Chapters 11-14.	slides	Richard Post
November 19, 2018	LUMC building 2, T-00-022	Hernán & Robins, Chapters 1-7.	notes	Aad van der Vaart

Papers to discuss in the future:

• Dawid (2020). Decision-theoretic foundations for statistical causality.
• Schölkopf (2019). Causality for machine learning.
• Frangakis, Rubin (2002). Principical stratification in causal inference.  and Pearl (2011). Principal stratification – a goal or a tool? and discussion.
• Wang and Blei (2019). The blessings of multiple causes, the discussion by Ogburn, Shpitser and Tchetgen Tchetgen, by D’Amour, and a response by Wang and Blei.
• Maathuis, Kalisch, Bühlmann (2009). Estimating high-dimensional intervention effects from observational data.
• Farrell (2015). Robust inference on average treatment effects with possibly more covariates than observations.
• Colombo, Maathuis, Kalisch, Richardson (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. 
• Maathuis, Colombo (2015). A generalized back-door criterion.
• Robins, Greenland (1992). Identifiability and exchangeability for direct and indirect effects. 
• Richardson, Robins (2010). Analysis of the binary instrumental variable model.
• VanderWeele (2019). Principles of confounder selection.
• Shortreed, Ertefaie (2017). Outcome-adaptive lasso: variable selection for causal inference.
• Liu, Kuramoto, Stuart (2014). An introduction to sensitivity analysis for unobserved confounding in non-experimental prevention research.
• Rosenbaum and Rubin (1983). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. (see also Rosenbaum’s Observational Studies, chapter 4)
• Hahn, Todd, Van der Klaauw (2001). Identification and estimation of treatment effects with a regression-discontinuity design. and Imbens, Lemieux (2008). Regression discontinuity designs: a guide to practice. 
• Sarvet, Wanis, Stensrud, Hernán (2020). A graphic description of partial exchangeability.