Causal Inference

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!

From TT 2025, the reading group is arranged in collaboration with the Copenhagen Pioneer Centre for SMARTbiomed! Featured speakers will rotate between both institutions, in addition to visiting guests as usual.

• 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

This Term

We are taking the summer break and will be back at October 17th, 2025!

Previous Talks

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)