Wednesday 27th March

Registration Desk open from 9:00 (in foyer of GW2 building)
Welcome session and all talks in HS/Keksdose building
Wed 9:45-10:00

Welcome (Vanessa Didelez)

Wed 10:00-10:40

Invited talk 1: Miguel Hernán (Harvard T.H. Chan School of Public Health, US)
Do you believe in causes? The distinction between causality and causal inference

Wed 10:40-11:20

Session 1 - Survival and multi-state modelling
Chair: Saskia le Cessie (Leiden University, The Netherlands)


Clémence Leyrat (London School of Hygiene and Tropical Medicine, UK)
Emulating a randomised trial from registry data to evaluate the effect of surgical treatment on survival among older lung cancer patients: results and methodological challenges


Johan Steen (Ghent University, Belgium)
Handling time-dependent exposures and confounders when estimating attributable fractions – bridging the gap between multi state and counterfactual modeling

Wed 11:20-11:50

Coffee break (in GW2 foyer)

Wed 11:50-12:50

Session 2 - Competing risks and dynamic interventions
Chair: Shaun Seaman (University of Cambridge, UK)


Mats J. Stensrud (Harvard University, US)
Separable effects: New estimands for causal inference in competing risk settings


Maja von Cube (University of Freiburg, Germany)
A conceptual framework for the population-attributable fraction in the presence of time-dependent exposures and competing risks


Michael Schomaker (University of Cape Town, South Africa)
Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions

Wed 12:50-14:00

Lunch (in canteen/‘Mensa’)

Wed 14:00-15:20

Session 3 – Difference in differences and synthetic control
Chair: Richard Grieve (London School of Hygiene and Tropical Medicine, UK)


Michael Zimmert (University of St.Gallen, Switzerland)
Difference-in-differences estimation with high-dimensional common trend confounding


Giulio Grossi (IRPET -Regional Institute for Economic Planning of Tuscany, Italy)
Direct and spillover effects using the synthetic control method in the presence of interference


Invited talk 2: Guido W. Imbens (Stanford Graduate School of Business, US)
Synthetic difference in differences

Wed 15:20-15:50

Coffee break (in GW2 foyer)

Wed 15:50-17:10

Session 4 – Interventions and hypothetical regimes
Chair: Nuala Sheehan (University of Leicester, UK)


Kjetil Røysland (University of Oslo, Norway)
Estimating effects from hypothetical treatment regimes in survival analysis with stochastic differential equations


Margarita Moreno-Betancur (University of Melbourne, Australia)
Policy-relevant interventional effects for evaluating interventions on multiple mediators: Application to adolescent self-harm and risk of later financial hardship


Invited talk 3: Philip Dawid (University of Cambridge, UK)
Causal inference isn’t what you think it is

Wed 17:10-19:00

Posters and reception (in GW2 foyer)

Thursday 28th March

All talks before lunch in HS/Keksdose building
Thu 9:00-10:20

Session 5 - Regression discontinuity designs
Chair: Finbarr Leacy (Health Products Regulatory Authority, Ireland)


Mariam Adeleke (University College London, UK)
Modelling time-to-event outcomes in an observational setting with a regression discontinuity design


Juan D.Díaz (University of Chile, Chile)
General discontinuity designs using covariates


Invited talk 4: Kate Tilling (University of Bristol, UK)
Challenges in applying RDD

Thu 10:20-10:50

Coffee break (in GW2 foyer)

Thu 10:50-12:30

Session 6 - Instrumental variables and heterogeneity
Chair: Sonja Swanson (Erasmus University Rotterdam, The Netherlands)


Stephen Burgess (University of Cambridge, UK)
Discovering causal mechanisms via contamination mixture modelling


Neil M. Davies (University of Bristol, UK)
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption


Michael Johnson (University of Wisconsin-Madison, US)
Detecting heterogeneous treatment effect with instrumental variables


Invited talk 5: Anirban Basu (University of Washington, US)
Heterogeneous treatment effects using continuous instrumental variables

Thu 12:30-13:30

Lunch (in canteen/‘Mensa’)

Thu 13:30-14:30

Parallel Session 7a – Mixed topics (HS/Keksdose)
Chair: Sabine Landau (King’s College London, UK)


Luke Keele (University of Pennsylvania, US)
Comparing covariate prioritization via matching to machine learning methods for causal inference using five empirical applications


Wen Wei Loh (Ghent University, Belgium)
Interventional effects models for mediation analysis with multiple mediators


Bas B. L. Penning de Vries (Leiden University, The Netherlands)
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Thu 13:30-14:30

Parallel Session 7b – Adjustment for confounding (GW2 room B3009)
Chair: Marloes Maathuis (ETH Zurich, Switzerland)


Leonard Henckel (ETH Zurich, Switzerland)
Graphical criteria for efficient total effect estimation via adjustment


Janine Witte (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Germany)
On efficient adjustment in causal graphs


Ian Shrier (McGill University, Canada)
Unmeasured confounding, bias amplification and model selection

Thu 14:30-15:50

Posters and coffee (in GW2 foyer)

Thu 15:50-17:30

Parallel Session 8a – Instrumental variables (HS/Keksdose)
Chair: Stephen Burgess (University of Cambridge, UK)


Chin Yang Shapland (University of Bristol, UK)
Bayesian model averaging for two-sample summary data Mendelian randomisation in the presence of pleiotropy


Elizabeth W. Diemer (Erasmus University Rotterdam, The Netherlands)
Application of the instrumental inequalities to a Mendelian randomization study with multiple proposed instruments


Eleanor Sanderson (University of Bristol, UK)
Testing for weak instruments in two sample summary data multivariable Mendelian randomisation


Matthew Tudball (University of Bristol, UK)
An interval estimation approach to selection bias in IV studies


Frank Windmeijer (University of Bristol, UK)
The confidence interval method for selecting valid instruments for instrumental variables estimation

Thu 15:50-17:30

Parallel Session 8b – Trials and designs (GW2 room B3009)
Chair: Werner Brannath (University of Bremen, Germany)


Chiara Bocci (University of Florence, Italy)
Marginal structural models in the presence of multiple treatments, with application to the analysis of export promotion programs


Björn Bornkamp (Novartis, Switzerland)
Bayesian inference for a principal stratum estimand –methods and sensitivity analyses


Kelly Van Lancker (Ghent University, Belgium)
Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomised clinical trial


Sabine Landau (King’s College London, UK)
Addressing treatment contamination in the design and analysis of trials of complex interventions


Anne Helby Petersen (University of Copenhagen, Denmark)
Sibling comparison designs: A viable path to causal effects?

Thu 19:00

Conference dinner (Emma am See / Emma at the Lake)

Friday 29th March

All talks in HS/Keksdose building
Fri 9:00-10:40

Session 9 – Graphs and causal discovery
Chair: Ronja Foraita (Leibniz Institute for Prevention Research and Epidemiology –BIPS, Germany)


Johannes Textor (Radboud University, The Netherlands)
A causal inference perspective on network deconvolution


Søren Wengel Mogensen (University of Copenhagen, Denmark)
Causal learning for linear SDEs


Federico Castelletti (Università Cattolica del Sacro Cuore,Italy)
Bayesian inference of DAG models for the estimation of causal effects


Invited talk 6: Marloes Maathuis (ETH Zurich, Switzerland)
Robust causal structure learning with some hidden variables

Fri 10:40-11:10

Coffee break (in GW2 foyer)

Fri 11:10-12:50

Session 10 - High-dimensional and machine learning
Chair: Fabrizia Mealli (University of Florence, Italy)


Helene Charlotte Rytgaard (University of Copenhagen, Denmark)
Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes


Johann Gagnon-Bartsch (University of Michigan, US)
Integrating experimental and observational data through machine learning


Alexander Volfovsky (Duke University, US)
Machine learning methods for causal inference from complex observational data


Invited talk 7: Andrea Rotnitzky (Di Tella University, Argentina, and Harvard School of Public Health, US)
A unifying approach for doubly-robust L1 regularized estimation of causal contrasts

Fri 13:00

Closing (Vanessa Didelez and Stephen Burgess)
With lunch afterwards


  1. Marija Glisic (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Germany)
    Mendelian randomization provides evidence for a causal role of dehydroepiandrosterone sulfate in decreased NT-proBNP levels in a Caucasian population
  2. Verena Zuber (Imperial College London, UK; University of Cambridge, UK)
    Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization
  3. Adam Mitchell (Uppsala University, Sweden)
    Is the effect of Mediterranean diet on hip fracture mediated through type 2 diabetes mellitus and body mass index?
  4. Monica Musio (University of Cagliari, Italy)
    Identifying causes of effects with mediators
  5. Sharon Remmelzwaal (Vrije Universiteit Amsterdam, The Netherlands)
    The mediating effect of low-grade inflammatory markers on the association between sex and cardiac function and structure
  6. Sara Benitez Majano (London School of Hygiene and Tropical Medicine, UK)
    Management of colorectal cancer in older patients: Exploring the role of comorbidity and the diagnostic route using mediation analysis
  7. Nicola Fitz-Simon (National University of Ireland Galway, Ireland)
    Mediation analysis of maternal depression and child neurodevelopment
  8. Ryan M. Andrews (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Germany)
    Insights into the cross-world assumption of causal mediation analysis: theoretical and practical considerations
  9. Felicitas Kühne (UMIT - University for Health Sciences, Medical Informatics and Technology, Austria)
    Assessing type and impact of biases potentially occurring when analyzing real world evidence: the case of second line treatment for ovarian cancer
  10. Mia S. Tackney (University of Southampton, UK)
    Sequential design of experiments for personalized medicine
  11. cancelled
  12. Imke Mayer (PLS University, France)
    Causal inference with missing values: treatment effect estimation of tranexamic acid on mortality for traumatic brain injury patients
  13. Sho Komukai (Osaka University, Japan)
    Doubly robust inference procedures for analyzing the cancer registry data
  14. Chi-Hun Kim (University of Oxford, UK)
    Causal inference for trends in disease incidence using complex observational data
  15. Thomas Klausch (Vrije Universiteit Amsterdam, The Netherlands)
    Validating optimal treatment regimes using multiple imputation
  16. Derek Hazard (University of Freiburg, Germany)
    Multi-state models and causal inference: prevention effect on burden of hospital infections
  1. Rik van Eekelen (Amsterdam University, The Netherlands)
    Reducing selection bias: turning the underlying heterogeneity to your advantage
  2. Zoltán Somogyvári (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary)
    Complete inference of causal relationships in dynamical systems
  3. Pantelis Samartsidis (University of Cambridge, UK)
    A Bayesian multivariate factor analysis model for evaluating an intervention using observational time-series data on multiple outcomes
  4. Marcell Stippinger (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary)
    Inferring causal relations with exact cross-mapping
  5. Zsigmond Benkő (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary)
    Detecting frequency-dependent cortical interactions with topological causal inference techniques
  6. Fabian Dablander (University of Amsterdam, The Netherlands)
    Centrality measures as a proxy for causal influence? A cautionary tale
  7. Moussa Laanani (CépiDc-Inserm France; Cochin Hospital, France)
    Collider and reporting biases involved in the analyses of cause of death associations in death certificates: An illustration with cancer and suicide
  8. cancelled
  9. Alexander Murray-Watters (GESIS – Leibniz Institute for the Social Sciences, Survey Design and Methodology, Germany)
    When Weighting Goes Wrong: The Implications of M-bias for Analyzing Survey Data
  10. Laura Forastiere (Yale University)
    Simulation-based sensitivity analysis for interference in observational studies with unmeasured links
  11. Silvia Noirjean (University of Florence, Italy)
    Disentangling spillover effects inside the principal strata in the presence of network data, with application to a field experiment on teens’ museum attendance
  12. Sebastián Martínez (University of Glasgow, UK)
    Causal inferential network analysis for public health
  13. Bas B.L. Penning de Vries (Leiden University, The Netherlands)
    Propensity score estimation using classification and regression trees in the presence of missing covariate data
  14. Jungyeon Choi (Leiden University, The Netherlands)
    How to handle missing data in propensity score analyses: a simulation study
  15. Emily Granger (University of Manchester, UK)
    A comparison of outcome-related diagnostics for propensity scores
  16. Massimo Cannas (University of Cagliari, Italy)
    Ordered matching for incomplete matching problems: a gender gap study
  17. Ghadeer Dawwas (University of Florida, US)
    Risk of acute kidney injury with SGLT2 inhibitors compared to sulfonylureas in patients with type 2 diabetes
  18. Rohan Arambepola (University of Oxford, UK)
    Causal inference for spatiotemporal epidemiological data
  19. Michael Knaus (University of St. Gallen, Switzerland)
    Machine learning estimation of heterogeneous causal effects: empirical Monte Carlo evidence
  20. Ian Nason (Harvard University, US)
    Using machine learning methods to improve propensity score estimation in observational studies when the treatment is rare
  21. Albert Prats-Uribe (University of Oxford, UK)
    Different approaches to minimise confounding when emulating a surgical randomised clinical trial: an application to partial vs total knee replacement 
  22. Jakob Runge (German Aerospace Center, Germany)
    Perspectives for causal inference in earth system sciences
  23. Lara Minkus (University of Bremen, Germany)
    A Trump effect on the EU’s popularity? The U.S. presidential election as a natural experiment
  24. Romin Pajouheshnia (Utrecht University, The Netherlands)
    A call for counterfactual reasoning when predicting patient prognosis