Causal Inference

In the causal inference reading group, we discuss papers and books related to causal inference and graphical models.

Organizer: Linying Yang (linying.yang at stats.ox.ac.uk). Reach out with any questions, or to be added to the internal mailing list!

• Time: Fridays at 10:00am UK / 11:00am DK (unless specified)
• Location: Meeting Room 3, 3rd Floor of the Statistics Department (24-29 St Giles')

Schedules

HT 2026

We are taking the winter break now. Will resume the series from Januaray 30th, 2026!

Date	Presenter	Title
30/01/2026	Jordan Penn
06/02/2026	Emma Prevot
13/02/2026	Fatima & Ruoxi	Special edition for Professor Frank Windmeijer's birthday!
20/02/2026	Break
27/02/2026	Daniel Manela
06/03/2026	Marcel Hedman

Previous Talks

MT 2025

Date	Presenter	Venue	Title
05/12/2025	Michael Sachs	Copenhagen	Deriving Linear Constraints in General DAGs for Discrete Settings
28/11/2025	Paweł Morzywołek	Copenhagen	Inference on Local Variable Importance Measures for Heterogeneous Treatment Effect
21/11/2025	Sydney Tucker	Oxford	Crisis Methods for Crisis Settings: Evaluating the Effectiveness of Interventions to Protect Children Amidst War and Crisis
14/11/2025	Christine Bang	Copenhagen	Causal discovery with temporal background knowledge
07/11/2025	Jakob Zeitler	Oxford	Causal Microscoping: Cost-optimal scientific inquiry using multi-fidelity causal active learning
31/10/2025	Marie Breum	Copenhagen	Nonparametric efficient estimation of the longitudinal front-door functional
24/10/2025	Yasin Ibrahim	Oxford	Causal Generative Modeling for Counterfactual Brain MRI Synthesis
17/10/2025	Alex Gruen	Oxford	ManyData: A multiple dimensional power likelihood to combine observational and experimental data

TT 2025

Date	Presenter	Venue	Title
06/06/2025	Linying Yang	Oxford	Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
30/05/2025	Mark Bech Knudsen	Copenhagen	On the limitations for causal inference in Cox models with time-varying treatment
23/05/2025	Maria Glymour	Copenhagen	Evidence Triangulation in Dementia Research
16/05/2025	Jeffrey Tse	Oxford	Selecting Invalid Instruments with Scaled Distances
09/05/2025	Michael Sachs	Copenhagen	Improved small-sample inference for causal bounds
02/05/2025	Cory McCartan	Oxford	Bias in Proximal and IV Estimators Under Imperfect Identification

HT 2025

Date	Presenter	Title	Paper(s)
21/03/2025	Alexis Bellot	The Limits of Predicting Agents from Behaviour
14/03/2025	Margot Stakenborg	The Time-Dependency of Causality
07/03/2025	Nicholas Roy	Active Causal Discovery with Sequential Bayesian Experimental Design	Tigas et al. (2022)
28/02/2025	BREAK
21/02/2025	Frank Windmeijer	On Selecting Invalid Instruments
14/02/2025	Xi Lin	Simulating Longitudinal Data from Marginal Structural Models
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)

MT 2024

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

TT 2024

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

HT 2024

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)

MT 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

TT 2023

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)

HT 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)

MT 2022

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)

TT 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)

HT 2022

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)

MT 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)

LV 2021

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

TT 2021

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)

Paper Repository

Proximal learning

• Identifying causal effects with proxy variables of an unmeasured confounder by Miao et al. (2018)

• Introduction to proximal causal inference by Tchetgen Tchetgen et al. (2020)

• Semi-parametric proximal causal inference by Cui et al. (2020)

• The Proximal ID Algorithm by Shpitser et al. (2021)

Invariant risk minimisation

• Invariant Risk Minimization by M. Arjovsky (2019)

• Invariant Representation Learning for Treatment Effect Estimation by C. Shi (2020)

• Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification by R. Adragna (2020)

• Treatment Effect Estimation Using Invariant Risk Minimization by A. Shah (2021)

• The Risks of Invariant Risk Minimization by E. Rosenfeld (2020)

• Does Invariant Risk Minimization Capture Invariance? by P. Kamath (2021)

Identifiability

• A General Identification Condition for Causal Effects by J. Tian and J. Pearl (2002)

• Interpretation and Identification of Causal Mediation by J. Pearl (2014)

• Identifiability and exchangeability for direct and indirect effects by Robins and Greenland (1992)

• Causal inference with a graphical hierarchy of interventions by Shpitser and Tchetgen Tchetgen (2016)

• A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects by Malinsky et al. (2019)

Semiparametric methods and influence functions

• Semiparametric Theory and Missing Data by Anastasios A. Tsiatis (2006)

• Tutorial: Deriving The Efficient Influence Curve for Large Models by Jonathan Levy (2019)

• Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis by Eric Tchetgen Tchetgen and Ilya Shpitser, (2012)

• Double/debiased machine learning for treatment and structural parameters by Chernozhukov et al. (2018)

• Estimating Identifiable Causal Effects through Double Machine Learning by Jung, Tian and Bareinboim (2021)

• A Semiparametric Approach to Interpretable Machine Learning by Sani, Lee, Nabi and Shpitser (2020)

• Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables by Bhattacharya, Nabi and Shpitser (2020)

• Understanding Black-box Predictions via Influence Functions by Koh and Liang (2017)

• Validating Causal Inference Models via Influence Functions by Alaa and van der Schaar (2019)