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EUROPEAN CAUSAL INFERENCE MEETING (EUROCIM) Causal Inference in Health, Economic and Social Sciences Programme 27 – 29 March 2019, Bremen, Germany
EUROPEAN CAUSAL INFERENCE MEETING (EUROCIM) Causal Inference in Health, Economic and Social Sciences
Programme
27 – 29 March 2019, Bremen, Germany
Wednesday 27th March Registration Desk open from 9:00 (in foyer of GW2 building) Welcome session and all talks in HS/Keksdose building
09:45-10:00 Welcome (Vanessa Didelez) 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
10:40-11:20 Session 1 – Survival and multi-state modelling
Chair: Saskia le Cessie (Leiden University, The Netherlands)
10:40-11:00 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
11:00-11:20 Johan Steen (Ghent University, Belgium)
Handling time-dependent exposures and confounders when estimating attributable fractions – bridging the gap between multi state and counterfactual modeling
11:20-11:50 Coffee break (in GW2 foyer) 11:50-12:50 Session 2 – Competing risks and dynamic interventions Chair: Shaun Seaman (University of Cambridge, UK)
11:50-12:10 Mats J. Stensrud (Harvard University, US) Separable effects: New estimands for causal inference in competing risk settings
12:10-12:30 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
12:30-12:50 Michael Schomaker (University of Cape Town, South Africa) Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions
Wednesday, 27th March
12:50-14:00 Lunch (in canteen/‘Mensa’) 14:00-15:20 Session 3 – Difference in differences and synthetic control
Chair: Richard Grieve (London School of Hygiene and Tropical Medicine, UK)
14:00-14:20 Michael Zimmert (University of St. Gallen, Switzerland)
Difference-in-differences estimation with high-dimensional common trend confounding
14:20-14:40 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
14:40-15:20 Invited Talk 2 – Guido W. Imbens (Stanford Graduate School of Business, US) Synthetic difference in differences 15:20-15:50 Coffee break (in GW2 foyer)
15:50-17:10 Session 4 – Interventions and hypothetical regimes
Chair: Nuala Sheehan (University of Leicester, UK) 15:50-16:10 Kjetil Røysland (University of Oslo, Norway)
Estimating effects from hypothetical treatment regimes in survival analysis with stochastic differential equations
16:10-16:30 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
16:30-17:10 Invited Talk 3 - Philip Dawid (University of Cambridge, UK) Causal inference isn’t what you think it is 17:10-19:00 Posters and reception (in GW2 foyer)
Thursday 28th March
All talks before lunch in HS/Keksdose building 09:00-10:20 Session 5 – Regression discontinuity designs
Chair: Finbarr Leacy (Health Products Regulatory Authority, Ireland)
09:00-09:20 Mariam Adeleke (University College London, UK) Modelling time-to-event outcomes in an observational setting with a regression discontinuity design
09:20-09:40 Juan D. Díaz (University of Chile, Chile)
General discontinuity designs using covariates 09:40-10:20 Invited Talk 4 – Kate Tilling (University of Bristol, UK) Challenges in applying RDD 10:20-10:50 Coffee break (in GW2 foyer) 10:50-12:30 Session 6 – Instrumental variables and heterogeneity
Chair: Sonja Swanson (Erasmus University Rotterdam, The Netherlands)
10:50-11:10 Stephen Burgess (University of Cambridge, UK)
Discovering causal mechanisms via contamination mixture modelling 11:10-11:30 Neil M. Davies (University of Bristol, UK)
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption
11:30-11:50 Michael Johnson (University of Wisconsin-Madison, US)
Detecting heterogeneous treatment effect with instrumental variables
11:50-12:30 Invited Talk 5 – Anirban Basu (University of Washington, US)
Heterogeneous treatment effects using continuous instrumental variables
12:30-13:30 Lunch (in canteen/‘Mensa’)
Thursday, 28th March
13:30-14:30 Parallel Session 7a – Mixed topics (HS/Keksdose) Chair: Sabine Landau (King’s College London, UK)
13:30-13:50 Luke Keele (University of Pennsylvania, US)
Comparing covariate prioritization via matching to machine learning methods for causal inference using five empirical applications
13:50-14:10 Wen Wei Loh (Ghent University, Belgium)
Interventional effects models for mediation analysis with multiple mediators
14:10-14:30 Bas B. L. Penning de Vries (Leiden University, The Netherlands)
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
13:30-14:30 Parallel Session 7b – Adjustment for confounding (GW2 room B3009) Chair: Marloes Maathuis (ETH Zurich, Switzerland)
13:30-13:50 Leonard Henckel (ETH Zurich, Switzerland) Graphical criteria for efficient total effect estimation via adjustment
13:50-14:10 Janine Witte (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Germany) On efficient adjustment in causal graphs
14:10-14:30 Ian Shrier (McGill University, Canada)
Unmeasured confounding, bias amplification and model selection
14:30-15:50 Posters and coffee (in GW2 foyer)
Thursday, 28th March
15:50-17:30 Parallel Session 8a – Instrumental variables (HS/Keksdose) Chair: Stephen Burgess (University of Cambridge, UK)
15:50-16:10 Chin Yang Shapland (University of Bristol, UK) Bayesian model averaging for two-sample summary data Mendelian randomisation in the presence of pleiotropy
16:10-16:30 Elizabeth W. Diemer (Erasmus University Rotterdam, The Netherlands)
Application of the instrumental inequalities to a Mendelian randomization study with multiple proposed instruments
16:30-16:50 Eleanor Sanderson (University of Bristol, UK)
Testing for weak instruments in two sample summary data multivariable Mendelian randomisation
16:50-17:10 Matthew Tudball (University of Bristol, UK)
An interval estimation approach to selection bias in IV studies
17:10-17:30 Frank Windmeijer (University of Bristol, UK) The confidence interval method for selecting valid instruments for instrumental variables estimation
15:50-17:30 Parallel Session 8b – Trials and designs (GW2 room B3009) Chair: Werner Brannath (University of Bremen, Germany)
15:50-16:10 Chiara Bocci (University of Florence, Italy) Marginal structural models in the presence of multiple treatments, with application to the analysis of export promotion programs
16:10-16:30 Björn Bornkamp (Novartis, Switzerland)
Bayesian inference for a principal stratum estimand – methods and sensitivity analyses
16:30-16:50 Kelly Van Lancker (Ghent University, Belgium)
Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomised clinical trial
16:50-17:10 Sabine Landau (King’s College London, UK)
Addressing treatment contamination in the design and analysis of trials of complex interventions
17:10-17:30 Anne Helby Petersen (University of Copenhagen, Denmark)
Sibling comparison designs: A viable path to causal effects? 19:00 Conference dinner (Emma am See / Emma at the lake)
Friday 29th March
All talks in HS/Keksdose building
09:00-10:40 Session 9 – Graphs and causal discovery Chair: Ronja Foraita (Leibniz Institute for Prevention Research and
Epidemiology – BIPS, Germany) 09:00-09:20 Johannes Textor (Radboud University, The Netherlands)
A causal inference perspective on network deconvolution 09:20-09:40 Søren Wengel Mogensen (University of Copenhagen, Denmark)
Causal learning for linear SDEs 09:40-10:00 Federico Castelletti (Università Cattolica del Sacro Cuore, Italy)
Bayesian inference of DAG models for the estimation of causal effects 10:00-10:40 Invited Talk 6 – Marloes Maathuis (ETH Zurich, Switzerland)
Robust causal structure learning with some hidden variables 10:40-11:10 Coffee break (in GW2 foyer) 11:10-12:50 Session 10 – High-dimensional and machine learning
Chair: Fabrizia Mealli (University of Florence, Italy) 11:10-11:30 Helene Charlotte Rytgaard (University of Copenhagen, Denmark)
Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes
11:30-11:50 Johann Gagnon-Bartsch (University of Michigan, US)
Integrating experimental and observational data through machine learning
11:50-12:10 Alexander Volfovsky (Duke University, US)
Machine learning methods for causal inference from complex observational data
12:10-12:50 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 13:00 Closing (Vanessa Didelez and Stephen Burgess) With lunch afterwards
Posters
Poster sessions Wed 17:10-19:00 and Thu 14:30-15:50 in GW2 foyer
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 Nadia Sourial (McGill University, Canada) How robustly do we verify the assumptions of the causal inference framework? Qualitative methods can provide a more in-depth and informed assessment
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
18 Rik van Eekelen (Amsterdam University, The Netherlands) Reducing selection bias: turning the underlying heterogeneity to your advantage
19 Zoltán Somogyvári (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary)
Complete inference of causal relationships in dynamical systems
20 Pantelis Samartsidis (University of Cambridge, UK) A Bayesian multivariate factor analysis model for evaluating an intervention using observational time-series data on multiple outcomes
21 Marcell Stippinger (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary) Inferring causal relations with exact cross-mapping
22 Zsigmond Benkő (Wigner Research Centre for Physics of the Hungarian Academy of Sciences, Hungary) Detecting frequency-dependent cortical interactions with topological causal inference techniques
23 Fabian Dablander (University of Amsterdam, The Netherlands) Centrality measures as a proxy for causal influence? A cautionary tale
24 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
26 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
27 Laura Forastiere (Yale University) Simulation-based sensitivity analysis for interference in observational studies with unmeasured links
28 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
29 Sebastián Martínez (University of Glasgow, UK) Causal inferential network analysis for public health
30 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
31 Jungyeon Choi (Leiden University, The Netherlands) How to handle missing data in propensity score analyses: a simulation study
32 Emily Granger (University of Manchester, UK) A comparison of outcome-related diagnostics for propensity scores
33 Massimo Cannas (University of Cagliari, Italy) Ordered matching for incomplete matching problems: a gender gap study
34 Ghadeer Dawwas (University of Florida, US) Risk of acute kidney injury with SGLT2 inhibitors compared to sulfonylureas in patients with type 2 diabetes
35 Rohan Arambepola (University of Oxford, UK) Causal inference for spatiotemporal epidemiological data
36 Michael Knaus (University of St. Gallen, Switzerland) Machine learning estimation of heterogeneous causal effects: empirical Monte Carlo evidence
37 Ian Nason (Harvard University, US) Using machine learning methods to improve propensity score estimation in observational studies when the treatment is rare
38 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
39 Jakob Runge (German Aerospace Center, Germany) Perspectives for causal inference in earth system sciences
40 Lara Minkus (University of Bremen, Germany) A Trump effect on the EU’s popularity? The U.S. presidential election as a natural experiment
41 Romin Pajouheshnia (Utrecht University, The Netherlands) A call for counterfactual reasoning when predicting patient prognosis
Atlantic
7Things
Munte
stop 6, 8 Hauptbahnhof
stop 8, 22 Crüsemannallee
Emma am See dinner
stop 6, 22 Universität/ Zentralbereich
stop 6, 8, 24 Am Stern
Hotel Munte
Hotel Atlantic Hotel 7Things
Novum Hotel
EuroCIM
Stops close to Emma am See, where the dinner takes place, are ‘Bremen Crüsemannallee’ (tram 8, bus 22), ‘Bremen H.-H.-Meier-Allee’ (tram 6) and ‘Bremen Am Stern’ (trams 6 and 8, bus 24). From there the restaurant can be reached after a short walk (1-2 km) through the park, or you can walk directly from the university (about 3 km). For those who want to walk, we will gather at 6:30 pm in front of the 7Things Hotel and walk from there.
stop 6, 22 H.-H.-Meier-Allee
