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Our seminar series usually takes place the first Friday of each month from noon until 2pm (unless otherwise indicated in the table below) in the School of Public Health. All are welcome and encouraged to attend. Please subscribe to our mailing list to become a member of the Causal Inference Research Group (CIRG) and receive notifications about our seminars.

Upcoming CIRG Meetings

Date & TimeLeaderTopicLocation
Friday, September 13, 2024
12:00 - 2:00 PM
Paul ZivichWhat is Causal Inference?McG 2301
Friday, October 4, 2024
12:00 - 2:00 PM
Alexander KeilRosenau 133
Friday, November 8, 2024
12:00 - 2:00 PM
Rosenau 133
Friday, December 6, 2024
12:00 - 2:00 PM
Sonja SwansonMHRC 2005

Past CIRG Meetings

Friday, April 12, 2024Maya MathurStanford UniversityA common-cause principle for eliminating selection bias in causal estimands through covariate adjustment
Friday, March 1, 2024Anqi ZhaoDuke University: The Fuqua School of BusinessRerandomization based on p-values from covariate balance tests
Friday, February 2, 2024Brian ReichNorth Carolina State UniversitySpatial Causal Inference, Wildland Fire Smoke Effects, and Health
Friday, January 12, 2024CIRL Lab MembersUniversity of North Carolina at Chapel HillCausal "Flash" Talks
Friday, December 1, 2023Enrique Schisterman, Robert Platt, and Rachael RossA Discussion on the Future of Causal Inference in Epidemiology, Biostatistics, and Beyond
Friday, November 3, 2023M. Elizabeth HalloranUniversity of WashingtonApproaches for Estimating Causal Effects under Interference
Friday, October 13, 2023Noel WeissUniversity of WashingtonIncorporating prior information into the analysis and interpretation of epidemiologic studies
Friday, September 8, 2023Michael HudgensUniversity of North Carolina at Chapel HillWhat is causal inference?
Friday, April 7, 2023Ellen CanigliaUniversity of Pennsylvania
Perelman School of Medicine
Emulating a sequence of target trials to avoid immortal time bias in pregnancy studies
Friday, March 10, 2023Lan WenUniversity of WaterlooMultiply robust estimators of time-varying treatment effects
Friday, February 3, 2023Roland MatsouakaDuke UniversityOverlap weights: what are we weighting for?
Friday, January 20, 2023Désiré KédagniUniversity of North Carolina at Chapel HillGeneralized Difference-in-Differences Models: Robust-Bounds
Friday, December 2, 2022John JacksonJohns Hopkins UniversityThe Observational Target Trial: A Conceptual Model for Measuring Disparity
Friday, November 4, 2022Shu YangNorth Carolina State UniversityTargeted Optimal Treatment Regime Learning Using Summary Statistics
Friday, October 7, 2022Ye WangUniversity of North Carolina at Chapel HillDesign-Based Inference for Spatial Experiments with Interference
Friday, September 2, 2022Bonnie Shook-SaUniversity of North Carolina at Chapel HillWhat is Causal Inference
Friday, April 1, 2022Lihua LeiStanford UniversityConformal inference of counterfactuals and individual treatment effects
Friday, March 18, 2022Elizabeth StuartJohns Hopkins UniversityThe role of design in policy evaluation: Applications to COVID-related and opioid policies
Friday, February 4, 2022Lina MontoyaUniversity of North Carolina at Chapel HillAnswering Causal Questions with SMART data: Illustration using the ADAPT-R Trial
Friday, January 14, 2022Oliver MaclarenUniversity of AucklandModels, identifiability, and estimability in causal inference
Friday, December 3, 2021Eric Tchetgen TchetgenThe Wharton School of University of PennsylvaniaProximal Causal Inference
Friday, November 5, 2021Laura BalzerUniversity of Massachusetts AmherstEvaluating Community-Based Interventions with Two-Stage TMLE
Friday, October 1, 2021Catherine LeskoJohns Hopkins EpidemiologyInternal & external validity: selection, generalizability, and transportability biases
Friday, September 3, 2021Stephen R ColeUNC EpidemiologyWhat is causal inference?
Friday, January 8, 2021Beth Ann GriffinRAND CorporationIdentifying optimal methods for estimating the effects of state laws on opioid-related outcomes: Moving beyond the classic difference-in-difference model
Friday, December 4, 2020Sonja SwansonErasmus UniversityToward clearer questions in Mendelian randomization studies
Friday, November 6, 2020Andrea RotnitzkyUniversidad Torcuato Di TellaOptimal adjustment sets in non-parametric causal graphical models
Friday, October 2, 2020Bonnie Shook-SaUNC BiostatisticsCausal Methods for the Design and Analysis of Observational Studies for Point Exposure Effects
Friday, September 4, 2020Charles PooleUNC EpidemiologyWhat is causal inference?
Friday, February 7, 2020 Fredrik Sävje Yale UniversityBalancing covariates in randomized experiments using the Gram-Schmidt walk
Friday, November 1, 2019Chanelle HoweBrown UniversitySelection bias in health disparities studies
Friday, October 4, 2019Edward KennedyCarnegie Mellon UniversityInfluence Functions and Machine Learning in Causal Inference
Friday, September 6, 2019Alex KeilUNC EpidemiologyWhat is causal inference?
Friday, May 10, 2019Jaffer ZaidiHarvard BiostatisticsDetecting Individual Principal Causal Effects Under `Truncation by Death' and Censoring Through Time
Friday, April 5, 2019Ashley NaimiUniversity of Pittsburgh Epidemiology Valid Causal Effect Estimates with Machine Learning Algorithms
Friday, March 1, 2019Issa DahabrehBrown University EpidemiologyGeneralizing trial findings in nested trial designs with sub-sampling of non-randomized individuals
Friday, February 1, 2019Lara BuchakUniversity of California Berkeley, PhilosophyRisk and Rationality
Friday, January 11, 2019Forrest CrawfordYale, Biostatistics, Ecology and Evolutionary Biology, ManagementMechanistic and agnostic inference for infectious disease epidemiology
Friday, December 7, 2018Noah GreiferUNC Quantitative PsychologyBeyond propensity scores: Optimization-based weights for marginal structural models
Friday, November 2, 2018Alex KeilUNC EpidemiologySuper learning: a practical guide
Friday, October 1, 2018Mark van der LaanUniversity of California Berkeley, BiostatisticsTargeted Learning utilizing Highly Adaptive Lasso
Friday, September 7, 2018Jess EdwardsUNC EpidemiologyA hitchhikers guide to causal inference (abridged)
Friday, May 4, 2018Avi FellerUniversity of California BerkeleyHow machine learning can be used to create more interpretable (yet still data-driven) approaches to causal inference and prediction
Friday, April 6, 2018Cynthia RudinDuke UniversityHow machine learning can be used to create more interpretable (yet still data-driven) approaches to causal inference and prediction
Friday, March 2, 2018Alan BrookhartUNC EpidemiologyTowards a grammar of design and analysis in epidemiology
Friday, February 2, 2018Noah HaberUNC Carolina Population CenterThe health research impact pathway: A (very) meta approach to understanding how research is generated, disseminated, consumed, and utilized
Friday, January 31, 2018Til Stürmer & Jess EdwardsUNC EpidemiologyNew Versus Prevalent User Designs In Pharmacoepidemiology: Time To Get Principled About When We Can Be Pragmatic
Friday, December 1, 2017Charlie PooleUNC EpidemiologyInteraction on the Interface Between Sufficient Causes and Potential Outcomes
Friday, November 3, 2017Elizabeth (Betsy) OgburnJohns Hopkins Biostatistics Social networks, causal inference, and chain graphs
Friday, October 6, 2017Etsuji SuzukiHarvard EpidemiologySufficient-Cause Model and Potential-Outcome Model
Friday September 8, 2017Daniel WestreichUNC EpidemiologyWhat is Causal Inference?
Friday May 19, 2017Bryan LauJohns Hopkins EpidemiologyReflecting on the role of causal inference
Friday April 7, 2017Matt Maciejewski and Valerie SmithDuke University and Durham Veterans Affairs Medical CenterApproaches to Longitudinal Matching
Friday March 3, 2017Alex KeilUNC EpidemiologyLeveraging machine learning algorithms for epidemiologic inference
Friday February 10, 2017Harsha ThirumurthyUNC Health Policy and ManagementCausal effects of civil conflicts on child health
Friday January 13, 2017Brian Barkley, Sujatro Chakladar, Bradley Saul and Michael HudgensUNC BiostatisticsCausal Inference with Interference
Friday December 2, 2016Elizabeth StuartJohns Hopkins BiostatisticsAssessing and enhancing the generalizability of randomized trials to target populations
Thursday, November 3, 2016James RobinsHarvard EpidemiologyPath-specific Causal Effects
Friday, October 7, 2016Alexander VolfovskyDuke Statistical ScienceCausal inference in the presence of networks: randomization and observation
Friday, September 9, 2016Michael HudgensUNC BiostatisticsAn Introduction to Causal Inference
Friday, May 6, 2016Doug Lauen and Fatih UnluUNC Public Policy, Abt Associates, IncEarly College High Schools at Scale: Probing Impacts and Generalizability with a Quasi-Experiment Benchmarked Against an RCT
Friday, April 1, 2016Donglin ZengUNC BiostatisticsPersonalized Medicine and SMART Designs
Friday, March 11, 2016Wen Wei LohUniversity of WashingtonA Finite Population Likelihood Ratio Test of the Sharp Null Hypothesis for Compliers
Friday, March 4, 2016Edward KennedyUniversity of Pennsylvania Wharton SchoolSemiparametric Theory and Empirical Processes in Causal Inference
Friday, February 5, 2016Alex KeilUNC EpidemiologyA Bayesian approach to the g-formula
Friday, January 8, 2016Michael Hudgens, Steve Cole and Tiffany BregerUNC Biostatistics and EpidemiologyOn the G-Null Paradox
Friday, December 4, 2015Dylan SmallUniversity of Pennsylvania Wharton SchoolDissonant Conclusions When Testing the Validity of an Instrumental Variable
Friday, November 6, 2015Daniel WestreichUNC EpidemiologyBig Data and Causal Inference
Friday, October 2, 2015Kai ZhangUNC Statistics and Operations ResearchUsing Split Samples and Evidence Factors in an Observational Study of Neonatal Outcomes
Friday, September 18, 2015Steve ColeUNC EpidemiologySo what is “Causal Inference”? [Introductions and Roundtable Discussion]
Friday, May 1, 2015Charlie PooleUNC EpidemiologyPublic Health Interaction
Friday, April 17, 2015Peter GilbertFred Hutchinson Cancer Research CenterPredicting Overall Vaccine Efficacy in a New Setting by Re-calibrating Effect Modifiers of Type-Specific Vaccine Efficacy
Friday, March 6, 2015Jeremy MoultonUNC Public PolicyA Regression Discontinuity Analysis of Virginia Elections
Monday, February 23, 2015Caleb MilesHarvard BiostatisticsQuantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program
Friday, February 6, 2015Fan LiDuke UniversityEvaluating the Effect of University Grants on Student Dropout: Evidence from a Regression Discontinuity Design Using Bayesian Principal Stratification Analysis
Friday, January 16, 2015Jacob BorBoston UniversityRegression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials
Wednesday, December 3, 2014Joe RigdonUNC BiostatisticsCausal inference for binary data with interference
Thursday, November 20, 2014Miguel HernanHarvard EpidemiologyComparative effectiveness of static and dynamic treatment strategies: an application of the parametric g-formula
Friday, October 3, 2014Eric LaberNCSU StatisticsFunctional feature construction for personalized treatment regimes
Friday, September 5, 2014Ashley NaimiMcGill EpidemiologyEstimating Controlled Direct Effects
Wednesday, April 23, 2014Thomas RichardsonUW StatisticsUnifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs)
Friday, April 4, 2014Eric Tchetgen TchetgenHarvard Biostatistics and EpidemiologyTaming selection bias in the health sciences: some new strategies
Friday, March 7, 2014Michele Jonsson-FunkUNC EpidemiologyTreatment effect heterogeneity: What interaction terms didn't tell you
Friday, February 7, 2014Daniel Westreich and Jess EdwardsUNC EpidemiologyTBA
Friday, January 3, 2014Amy RichardsonUNC BiostatisticsBounds and Sensitivity Analysis
Thursday, December 5, 2013Tyler VanderWeeleHarvard EpidemiologySurrogate Measures and Consistent Surrogates
Friday, November 1, 2013Alan BrookhartUNC EpidemiologyRisk Score Block Randomization
Friday, October 4, 2013Charlie PooleUNC EpidemiologySingle world intervention graphs (SWIGs)
Friday, September 6, 2013Steve ColeUNCAll of the secrets I know about risk
Friday, June 7, 2013NANARoundtable
Friday, May 3, 2013Stephen ColeUNCRefined risk differences and ratios in cohort studies with competing risks
Friday, April 5, 2013Wei SunUNCHigh dimensional DAGs
Friday, March 1, 2013Michael HudgensUNCInterference
Friday, February 1, 2013Susan GruberHarvardTargeted maximum likelihood
Friday, January 4, 2013NANARoundtable
Friday, December 7, 2012Butch TsiatisNCSUA robust method for estimating optimal treatment regimes
Friday, November 2, 2012Lauren CainHarvardWhen to start? A systematic approach to the comparison of dynamic regimes using observational data Methods Clinical
Friday, October 5, 2012Andrew AllenDukeControl for Confounding in Case-Control Studies Using the Stratification Score, a Retrospective Balancing Score
Friday, September 7, 2012Jess Edwards and Alex KeilUNC Department of EpidemiologyComparison of three causal models to control time-varying confounding in a cohort of bone marrow transplant recipients
Friday, June 8, 2012Daniel WestreichDept. of Obstetrics & Gynecology,
Friday, May 4, 2012Miguel HernanHarvard Department of EpidemiologyA Discussion of when to start therapy with respect to biomarkers: the case of HIV: Lancet, NEJM, and AIM.
Thursday, March 29, 2012Rhian DanielThe London School of Hygiene and Tropical MedicineUsing causal diagrams to guide analysis in missing data problems
Friday, March 2, 2012Jay KaufmanMcGill Department of Epidemiology, Biostatistics and Occupational HealthNon-Collapsibility of Odds Ratios: Comparison of Marginal with Conditional Estimates from Logistic Regression Models
Friday, February 3, 2012Tyler VanderWeeleHarvard Department of EpidemiologyWhat is Confounding?
Friday, December 2, 2011Eric Tchetgen TchetgenHarvard Department of EpidemiologyA General Class of Methods for Causal Mediation Analysis: identification, efficiency, multiple robustness and sensitivity analysis
Friday, November 4, 2011Fan LiDuke University Department of Statistical ScienceA Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference
Friday, October 7, 2011Sam Field & Matt McBeeUNC: Frank Porter Graham Child Development Institute & East Tennessee State University: PsychologyFinite Sample Bias in Propensity Score Matching/Weighting
Friday, September 2, 2011Stephen ColeUNC: EpidemiologyCausal Inference
Friday, June 3, 2011NANARound table
Friday, May 6, 2011Dustin LongUNC: BiostatisticsComparing Competing Risks Outcomes Within Principal Strata
Friday, April 1, 2011Whitney RobinsonUNCA causal approach to health disparities research
Friday, March 4, 2011M. Elizabeth HalloranUniv. Of WashingtonCausal inference for vaccine effects for infectiousness
Friday, February 4, 2011Jason FineUNC: BiostatisticsSensitivity testing for nonidentifiable models
Thursday, January 6, 2011Charles PooleUNC: EpidemiologyEffect Modification in Sufficient-Cause and Potential-Risk Frameworks
Friday, December 3, 2010Stephen ColeUNC: EpidemiologyMSM Case cohort studies
Wednesday, November 3, 2010James RobinsHarvardDirect and indirect effects
Friday, October 1, 2010Michael KosorokUNC: BiostatisticsPersonalized Medicine and Clinical Trials
Friday, September 10, 2010E. Michael FosterUNCGeneralized Methods of Moments and Causal Inference
Friday, June 4, 2010Tracy NolenUNC: BiostatisticsRandomization-based inference in principal strata
Friday, May 14, 2010Haitao ChuUNC: BiostatisticsMeasurement error
Friday, April 9, 2010Tyler VanderweeleHarvard Department of EpidemiologyMediation and interference
Friday, March 5, 2010Alan BrookhartBias inflation/IV
Friday, February 5, 2010Chanelle HoweUNC: EpidemiologyMSM for alcohol/HIV
Friday, January 8, 2010Daniel WestreichParametric G formula
Friday, December 4, 2009Stephen ColeUNC: EpidemiologyRCTs and generalizability
Friday, November 6, 2009Bryan ShepherdVanderbiltWhen to start HAART?
Friday, October 2, 2009Kenneth BollenStructural equations
Friday, September 4, 2009Daniel AlmirallTime varying effect mod.
Friday, August 7, 2009E. Michael FosterUNCInstrumental variables
Friday, July 3, 2009Stephen ColeUNC: EpidemiologyMSM with measurement-error
Friday, June 5, 2009NANARound table
Friday, May 1, 2009Michele FunkDoubly robust inference
Friday, April 3, 2009NANARound table
Friday, March 6, 2009E. Michael FosterUNCHeckman’s model
Friday, February 6, 2009Charles PooleUNC: EpidemiologyWhat is gender bias?
Tuesday, January 6, 2009Stephen ColeUNC: EpidemiologyDirect/Indirect effects
Tuesday, December 2, 2008Til SturmerNurses Health Study
Tuesday, November 4, 2008E. Michael FosterUNCCausal diagrams
Tuesday, October 7, 2008Michael HudgensUNC: BiostatisticsPrincipal stratification
Tuesday, September 2, 2008Stephen ColeUNC: EpidemiologyPositivity
Thursday, August 7, 2008Michael HudgensUNC: BiostatisticsInterference
Tuesday, July 22, 2008Stephen ColeUNC: EpidemiologyConsistency
Tuesday, July 8, 2008Stephen ColeUNC: EpidemiologyPotential outcomes