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Causal Inference
Vik Shirvaikar edited this page Jan 29, 2025
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In the causal inference reading group, we discuss papers and books related to causal inference and graphical models.
Organizers: Xi Lin (xi.lin at stats.ox.ac.uk) and Vik Shirvaikar (vik.shirvaikar at spc.ox.ac.uk). Contact us with any questions, or to be added to the internal mailing list!
- Time: every Friday 10:00-11:00 GMT/BST (unless specified)
- Location: Meeting Room 3, 3rd Floor of the Statistics Department (24-29 St Giles')
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 07/02/2025 | Emma Prevot | The Arrow of Time: Causality and Physics | |
| 31/01/2025 | Jakob Zeitler | Expressing Cost of Causal Assumptions Through Partial Identification | |
| 24/01/2025 | BREAK | ||
| 17/01/2025 | Yuhao Wang | Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates | Lu et al. (2023) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 06/12/2024 | Kosuke Imai | Causal Representation Learning with Generative Artificial Intelligence | Imai and Nakamura (2024) |
| 29/11/2024 | Joel Dyer and Nick Bishop | Accelerating decision-making with causal abstraction (part 2) | Zennaro et al. (2024) |
| 22/11/2024 | BREAK | ||
| 15/11/2024 | Vik Shirvaikar | Philosophy and causality: an introduction | |
| 08/11/2024 | Laura Battaglia and Dan Manela | Marginal Causal Flows for Validation and Inference | |
| 01/11/2024 | Joel Dyer and Nick Bishop | Accelerating decision-making with causal abstraction (part 1) | Dyer et al. (2023) |
| 25/10/2024 | Robin Evans | Marginal log-linear parameters: lessons for general distributions | |
| 18/10/2024 | Qinyu Li | Causal inference with continuous treatments |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 14/06/2024 | Zijian Guo | Robust Causal Inference with Possibly Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling | Guo (2023) |
| 07/06/2024 | Linying Yang | Estimand selection | |
| 31/05/2024 | BREAK | ||
| 24/05/2024 | Jack Foxabbott | A Causal Model of Theory-of-Mind in AI Agents | |
| 17/05/2024 | Jeffrey Tse | Instrumental Variables Estimation with Some Invalid Instruments | |
| 10/05/2024 | Ziwei Mei | Robust Instrumental Analysis for Multiple Treatments: Identification Conditions and Uniform Inference | |
| 03/05/2024 | Anthony Webster | Causal attribution fractions - estimating the impact of smoking and BMI on the prevalence of diseases | Webster (2022) |
| 26/04/2024 | Causal roundtable | Lightning talks from Jack Foxabbott, Lucile Ter-Minassian, and Xi Lin |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 08/03/2024 | Frank Windmeijer | The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exogeneity or Exclusion Restriction | Apfel and Windmeijer (2022) |
| 01/03/2024 (4 PM) | Oscar Clivio | Causal reasoning in LLMs | Yang et al. (2024) |
| 23/02/2024 | Andrew Yiu | Intro to semiparametric theory (part 2) | |
| 16/02/2024 | Ziyu Wang | Selection of valid instruments | Windmeijer (2019) Windmeijer et al. (2019) Windmeijer et al. (2021) |
| 09/02/2024 | Andrew Yiu | Intro to semiparametric theory (part 1) | |
| 02/02/2024 | Robin Evans | Causal Discovery with Latent Variables | Chen et al. (2022) Dong et al. (2023) Huang et al. (2022) Xie et al. (2020) |
| 26/01/2024 | Dan Manela and Vik Shirvaikar | Double/debiased machine learning (part 2) | Chernozhukov et al. (2018) |
| 19/01/2024 | Dan Manela and Vik Shirvaikar | Double/debiased machine learning (part 1) | Chernozhukov et al. (2018) |
| 12/01/2024 | Linying Yang | Offline policy learning | Jin et al. (2023) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 01/12/2023 | Xi Lin | Data fusion: method review and case study | |
| 24/11/2023 | BREAK | ||
| 17/11/2023 | Jack Foxabbott | Amortized Inference for Causal Structure Learning | Lorch at al. (2022) |
| 10/11/2023 | BREAK | ||
| 03/11/2023 | BREAK | ||
| 27/10/2023 | Oscar Clivio | Towards Representation Learning for General Weighting Problems in Causal Inference | |
| 20/10/2023 | BREAK | ||
| 13/10/2023 | Vik Shirvaikar and Dan Manela | Causal reinforcement learning |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 26/05/2023 | Vik Shirvaikar | Synthetic controls | Abadie et al. (2010) |
| 19/05/2023 | Oscar Clivio | The Balancing Act in Causal Inference | Ben-Michael et al. (2021) |
| 12/05/2023 | BREAK | ||
| 05/05/2023 | Jakob Zeitler | Introduction to Partial Identification | Zeitler and Silva (2022); Padh et al. (2023) |
| 28/04/2023 | Linying Yang | CausalEGM: A General Causal Inference Framework by Encoding Generative Modelling | Liu, Chen and Wong (2023) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 24/03/2023 | Robin Evans | Parameterizing and Simulating from Causal Models | Evans and Didelez (2021) |
| 17/03/2023 | Ryan Carey | Network nonlocality via rigidity of token counting and color matching | Renou and Beigi (2022) |
| 03/03/2023 | Aleks Kissinger | Black-box causal reasoning with string diagrams | |
| 24/02/2023 | Daniel Manela | Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score | Schuler (2021) |
| 17/02/2023 | Xi Lin | Negative Control Outcomes | |
| 10/02/2022 | BREAK | ||
| 03/02/2023 | Vik Shirvaikar | Targeted Maximum Likelihood Estimation (TMLE) | |
| 27/01/2023 | Oscar Clivio | Dynamic treatment regimes | |
| 20/01/2023 | Zhongyi Hu | Randomization tests | Yao and Zhao (2022), Yao and Zhao (2021) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 25/11/2022 | Robin Evans | Nested Markov Properties for Acyclic Directed Mixed Graphs | Richardson et al. (2022) |
| 18/11/2022 | Vik Shirvaikar | Causal Forests | Wager and Athey (2018); Athey, Tibshirani and Wager (2018) |
| 11/11/2022 | Frank Windmeijer | Falsification Adaptive Set | Masten and Poirier (2021) |
| 04/11/2022 | Dan Manela | Mitigating hidden confounders in Multiple Causal Inference | Wang and Blei (2018); Bia et al. (2020) |
| 28/10/2022 | Yuchen Zhu (UCL) | Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach | Zhu et al. (2022) |
| 21/10/2022 | Xi Lin | Decision-theoretic perspective of causal inference | Dawid (2020) |
| 14/10/2022 | Zhongyi Hu | Markov equivalence for margins of DAGs (Recording) | Hu and Evans (2020); Claassen and Bucur (2022); Wienöbst et al. (2022) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 17/06/2022 | Bruce Liu | Quantile Methods (Recording) | Chernozhukov and Hansen (2013) |
| 10/06/2022 | Yiqi Lin | On the instrumental variable estimation with potentially many (weak) and some invalid instruments | |
| 03/06/2022 | BREAK | ||
| 27/05/2022 | Robin Evans | Inflation Technique for Causal Inference (Recording) | Wolfe et.al.(2016); Navascues and Wolfe (2017) |
| 20/05/2022 | Oscar Clivio | Neural Score Matching for High-Dimensional Causal Inference | Clivio et al. (2021) |
| 13/05/2022 | Ryan Carey | Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility (Recording) | Halpern and Kleiman-Weiner (2018) |
| 06/05/2022 | Frank Windmeijer | Proximal Learning (Recording) | Mastouri et al. (2021) |
| 29/04/2022 | Xi Lin | Bespoke Instrumental Variables (Recording) | Richardson and Tchetgen Tchetgen (2021) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 01/04/2022 | Faaiz Taufiq | Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (Recording) | Zhang and Bareinboim (2019) |
| 25/03/2022 | BREAK | ||
| 18/03/2022 | Jake Fawkes | Foundations of Structural Causal Models with Cycles and Latent Variables | |
| 11/03/2022 | Bohao Yao | Hierachy of identifiability in linear SEMs | Yao & Evans (2021); Foygel et al. (2012); Foygel et al. (2022); Drton et al. (2011) |
| 04/03/2022 | Robert Hu | End-to-End Causality | Gruber and van der Laan (2009); Geffner et al. (2022) |
| 25/02/2022 | Zhongyi Hu | Constraint-Based Causal Discovery using Partial Ancestral Graphs in the Presence of Cycles | Mooij and Claassen (2020) |
| 18/02/2022 | Xi Lin | Combining Randomized and Observational Studies | Rosenman et al. (2018); Kallus et al. (2018); Peysakhovich and Lada (2016) |
| 11/02/2022 | Bruce Liu | Nonlinear IV Estimation for Mendelian Randomization | Staley and Burgess (2017); Sun et al. (2019) |
| 04/02/2022 | Robin Evans | Causal Survival Analysis | Keogh et al. (2021) |
| 28/01/2022 | BREAK | ||
| 21/01/2022 | Oscar Clivio | Proximal Causal Learning with Kernels:Two-Stage Estimation and Moment Restriction | Mastouri et al. (2021) |
| 14/01/2022 | Ryan Carey | Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness | |
| 07/01/2022 | Faaiz Taufiq | Conformal Inference of Counterfactuals and Individual Treatment Effects | Lei et al. (2021) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 10/12/2021 | Jake Fawkes | Ignorability and Causal Fairness | Fawkes et al. (2021) |
| 03/12/2021 | Bohao Yao | Maximum Likelihood Estimations in Linear Structural Equation Models | Drton et al. (2009), Drton et al. (2019) |
| 26/11/2021 | Zhongyi Hu | Maximal Ancestral Graph Structure Learning via Exact Search | Rantanen et al. (2021) |
| 19/11/2021 | Robert Hu | Causal Discovery | |
| 12/11/2021 | Robin Evans | Proximal Causal Inference | Cui et al. (2020) |
| 04/11/2021 (2 PM) | Xi Lin | Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes | Athey et al. (2020) |
| 28/10/2021 | Jake Fawkes | Invariant risk minimization | Arjovsky et al. (2019) |
| 21/10/2021 | Zhongyi Hu | Causal inference by using invariant prediction | Peters et al. (2016) |
| 07/10/2021 | Faaiz Taufiq | Path specific effects | Avin, Shpitser, Pearl. (2005); Shpitser and Pearl (2006); Shpitser and Tchetgen Tchetgen (2016) |
| 30/09/2021 | BREAK | ||
| 23/09/2021 | Ryan Carey | po-Calculus and Path Specific Effects | Malinsky et al. (2019) |
| 16/09/2021 | Robin Evans | Identifiability with hidden variables and selection bias | Evans and Didelez (2015) |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 26/08/2021 | Bohao Yao | Identification Conditions | Tian and Pearl (2002) |
| 19/08/2021 (2 PM) | Zhongyi Hu | Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables | Bhattacharya et al. (2020) |
| 12/08/2021 | Robin Evans | Causal ID Algorithm | Jung, Tian and Bareinboim, 2021 |
| 05/08/2021 | Jake Fawkes | Explainability via Influence Functions | Koh and Liang, 2017; Alaa and van der Schaar, 2019 |
| 29/07/2021 (11:30 AM) | Oscar Clivio | Double/debiased machine learning for treatment and structural parameters |
| Date | Presenter | Title | Paper(s) |
|---|---|---|---|
| 01/07/2021 | Jake Fawkes | Semiparametric theory for causal mediation analysis | Tchetgen Tchetgen and Shpitser, 2012 |
| 24/06/2021 | BREAK | ||
| 17/06/2021 (11:30 AM) | Robin Evans | Semiparametric Theory and Missing Data (chapter 13) and Levy's Tutorial | Levy, 2019 |
| 10/06/2021 | Zhongyi Hu | Semiparametric Theory and Missing Data (chapter 8) | |
| 03/06/2021 | Bohao Yao | Semiparametric Theory and Missing Data (chapter 7) | |
| 27/05/2021 | Bruce Liu | Semiparametric Theory and Missing Data (chapter 6) | |
| 20/05/2021 | Oscar Clivio | Semiparametric Theory and Missing Data (chapter 5) | |
| 13/05/2021 | Jake Fawkes | Semiparametric Theory and Missing Data (chapter 4) | |
| 06/05/2021 | Robin Evans | Semiparametric Theory and Missing Data (chapter 3) |
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Identifying causal effects with proxy variables of an unmeasured confounder by Miao et al. (2018)
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Introduction to proximal causal inference by Tchetgen Tchetgen et al. (2020)
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Semi-parametric proximal causal inference by Cui et al. (2020)
-
The Proximal ID Algorithm by Shpitser et al. (2021)
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Invariant Risk Minimization by M. Arjovsky (2019)
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Invariant Representation Learning for Treatment Effect Estimation by C. Shi (2020)
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Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification by R. Adragna (2020)
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Treatment Effect Estimation Using Invariant Risk Minimization by A. Shah (2021)
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The Risks of Invariant Risk Minimization by E. Rosenfeld (2020)
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Does Invariant Risk Minimization Capture Invariance? by P. Kamath (2021)
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A General Identification Condition for Causal Effects by J. Tian and J. Pearl (2002)
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Interpretation and Identification of Causal Mediation by J. Pearl (2014)
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Identifiability and exchangeability for direct and indirect effects by Robins and Greenland (1992)
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Causal inference with a graphical hierarchy of interventions by Shpitser and Tchetgen Tchetgen (2016)
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A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects by Malinsky et al. (2019)
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Semiparametric Theory and Missing Data by Anastasios A. Tsiatis (2006)
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Tutorial: Deriving The Efficient Influence Curve for Large Models by Jonathan Levy (2019)
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Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis by Eric Tchetgen Tchetgen and Ilya Shpitser, (2012)
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Double/debiased machine learning for treatment and structural parameters by Chernozhukov et al. (2018)
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Estimating Identifiable Causal Effects through Double Machine Learning by Jung, Tian and Bareinboim (2021)
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A Semiparametric Approach to Interpretable Machine Learning by Sani, Lee, Nabi and Shpitser (2020)
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Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables by Bhattacharya, Nabi and Shpitser (2020)
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Understanding Black-box Predictions via Influence Functions by Koh and Liang (2017)
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Validating Causal Inference Models via Influence Functions by Alaa and van der Schaar (2019)