CIRG Seminars
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 & Time | Leader | Topic | Location |
|---|---|---|---|
| Friday, April 25, 2025 12:15 - 2:00 PM | Alan Brookhart | SPH - Rosenau Hall - 230 Mayes Center |
Past CIRG Meetings
| Date | Leader | Affiliation | Topic |
|---|---|---|---|
| Thursday, March 6, 2025 | Miguel Hernán | Harvard T.H. Chan School of Public Health | How to make people immortal and why it is not a good idea: Improving the causal analyses of healthcare databases Recording Passcode: $0u&gQ.D |
| Friday, February 7, 2025 | Fan Li | Yale | How to achieve model-robust inference in stepped wedge trials with model-based methods |
| Friday, January 10, 2025 | CIRL Members | University of North Carolina at Chapel Hill | Causal Flash Talks |
| Friday, December 6, 2024 | Sonja Swanson | University of Pittsburgh | Informing suicide prevention strategies when randomized trials are not feasible |
| Friday, November 8, 2024 | Arran Zeyu Wang | University of North Carolina at Chapel Hill | Understanding and Supporting Visual Causal Inference Using Counterfactuals |
| Friday, October 4, 2024 | Alexander Keil | US National Cancer Institute | Multiple attempts at causal effect estimation with multiple continuous exposures |
| Friday, September 13, 2024 | Paul Zivich | University of North Carolina at Chapel Hill | What is causal inference? |
| Friday, April 12, 2024 | Maya Mathur | Stanford University | A common-cause principle for eliminating selection bias in causal estimands through covariate adjustment |
| Friday, March 1, 2024 | Anqi Zhao | Duke University: The Fuqua School of Business | Rerandomization based on p-values from covariate balance tests |
| Friday, February 2, 2024 | Brian Reich | North Carolina State University | Spatial Causal Inference, Wildland Fire Smoke Effects, and Health |
| Friday, January 12, 2024 | CIRL Lab Members | University of North Carolina at Chapel Hill | Causal "Flash" Talks |
| Friday, December 1, 2023 | Enrique Schisterman, Robert Platt, and Rachael Ross | A Discussion on the Future of Causal Inference in Epidemiology, Biostatistics, and Beyond | |
| Friday, November 3, 2023 | M. Elizabeth Halloran | University of Washington | Approaches for Estimating Causal Effects under Interference |
| Friday, October 13, 2023 | Noel Weiss | University of Washington | Incorporating prior information into the analysis and interpretation of epidemiologic studies |
| Friday, September 8, 2023 | Michael Hudgens | University of North Carolina at Chapel Hill | What is causal inference? |
| Friday, April 7, 2023 | Ellen Caniglia | University of Pennsylvania Perelman School of Medicine | Emulating a sequence of target trials to avoid immortal time bias in pregnancy studies |
| Friday, March 10, 2023 | Lan Wen | University of Waterloo | Multiply robust estimators of time-varying treatment effects |
| Friday, February 3, 2023 | Roland Matsouaka | Duke University | Overlap weights: what are we weighting for? |
| Friday, January 20, 2023 | Désiré Kédagni | University of North Carolina at Chapel Hill | Generalized Difference-in-Differences Models: Robust-Bounds |
| Friday, December 2, 2022 | John Jackson | Johns Hopkins University | The Observational Target Trial: A Conceptual Model for Measuring Disparity |
| Friday, November 4, 2022 | Shu Yang | North Carolina State University | Targeted Optimal Treatment Regime Learning Using Summary Statistics |
| Friday, October 7, 2022 | Ye Wang | University of North Carolina at Chapel Hill | Design-Based Inference for Spatial Experiments with Interference |
| Friday, September 2, 2022 | Bonnie Shook-Sa | University of North Carolina at Chapel Hill | What is Causal Inference |
| Friday, April 1, 2022 | Lihua Lei | Stanford University | Conformal inference of counterfactuals and individual treatment effects |
| Friday, March 18, 2022 | Elizabeth Stuart | Johns Hopkins University | The role of design in policy evaluation: Applications to COVID-related and opioid policies |
| Friday, February 4, 2022 | Lina Montoya | University of North Carolina at Chapel Hill | Answering Causal Questions with SMART data: Illustration using the ADAPT-R Trial |
| Friday, January 14, 2022 | Oliver Maclaren | University of Auckland | Models, identifiability, and estimability in causal inference |
| Friday, December 3, 2021 | Eric Tchetgen Tchetgen | The Wharton School of University of Pennsylvania | Proximal Causal Inference |
| Friday, November 5, 2021 | Laura Balzer | University of Massachusetts Amherst | Evaluating Community-Based Interventions with Two-Stage TMLE |
| Friday, October 1, 2021 | Catherine Lesko | Johns Hopkins Epidemiology | Internal & external validity: selection, generalizability, and transportability biases |
| Friday, September 3, 2021 | Stephen R Cole | UNC Epidemiology | What is causal inference? |
| Friday, January 8, 2021 | Beth Ann Griffin | RAND Corporation | Identifying optimal methods for estimating the effects of state laws on opioid-related outcomes: Moving beyond the classic difference-in-difference model |
| Friday, December 4, 2020 | Sonja Swanson | Erasmus University | Toward clearer questions in Mendelian randomization studies |
| Friday, November 6, 2020 | Andrea Rotnitzky | Universidad Torcuato Di Tella | Optimal adjustment sets in non-parametric causal graphical models |
| Friday, October 2, 2020 | Bonnie Shook-Sa | UNC Biostatistics | Causal Methods for the Design and Analysis of Observational Studies for Point Exposure Effects |
| Friday, September 4, 2020 | Charles Poole | UNC Epidemiology | What is causal inference? |
| Friday, February 7, 2020 | Fredrik Sävje | Yale University | Balancing covariates in randomized experiments using the Gram-Schmidt walk |
| Friday, November 1, 2019 | Chanelle Howe | Brown University | Selection bias in health disparities studies |
| Friday, October 4, 2019 | Edward Kennedy | Carnegie Mellon University | Influence Functions and Machine Learning in Causal Inference |
| Friday, September 6, 2019 | Alex Keil | UNC Epidemiology | What is causal inference? |
| Friday, May 10, 2019 | Jaffer Zaidi | Harvard Biostatistics | Detecting Individual Principal Causal Effects Under `Truncation by Death' and Censoring Through Time |
| Friday, April 5, 2019 | Ashley Naimi | University of Pittsburgh Epidemiology | Valid Causal Effect Estimates with Machine Learning Algorithms |
| Friday, March 1, 2019 | Issa Dahabreh | Brown University Epidemiology | Generalizing trial findings in nested trial designs with sub-sampling of non-randomized individuals |
| Friday, February 1, 2019 | Lara Buchak | University of California Berkeley, Philosophy | Risk and Rationality |
| Friday, January 11, 2019 | Forrest Crawford | Yale, Biostatistics, Ecology and Evolutionary Biology, Management | Mechanistic and agnostic inference for infectious disease epidemiology |
| Friday, December 7, 2018 | Noah Greifer | UNC Quantitative Psychology | Beyond propensity scores: Optimization-based weights for marginal structural models |
| Friday, November 2, 2018 | Alex Keil | UNC Epidemiology | Super learning: a practical guide |
| Friday, October 1, 2018 | Mark van der Laan | University of California Berkeley, Biostatistics | Targeted Learning utilizing Highly Adaptive Lasso |
| Friday, September 7, 2018 | Jess Edwards | UNC Epidemiology | A hitchhikers guide to causal inference (abridged) |
| Friday, May 4, 2018 | Avi Feller | University of California Berkeley | How machine learning can be used to create more interpretable (yet still data-driven) approaches to causal inference and prediction |
| Friday, April 6, 2018 | Cynthia Rudin | Duke University | How machine learning can be used to create more interpretable (yet still data-driven) approaches to causal inference and prediction |
| Friday, March 2, 2018 | Alan Brookhart | UNC Epidemiology | Towards a grammar of design and analysis in epidemiology |
| Friday, February 2, 2018 | Noah Haber | UNC Carolina Population Center | The health research impact pathway: A (very) meta approach to understanding how research is generated, disseminated, consumed, and utilized |
| Friday, January 31, 2018 | Til Stürmer & Jess Edwards | UNC Epidemiology | New Versus Prevalent User Designs In Pharmacoepidemiology: Time To Get Principled About When We Can Be Pragmatic |
| Friday, December 1, 2017 | Charlie Poole | UNC Epidemiology | Interaction on the Interface Between Sufficient Causes and Potential Outcomes |
| Friday, November 3, 2017 | Elizabeth (Betsy) Ogburn | Johns Hopkins Biostatistics | Social networks, causal inference, and chain graphs |
| Friday, October 6, 2017 | Etsuji Suzuki | Harvard Epidemiology | Sufficient-Cause Model and Potential-Outcome Model |
| Friday September 8, 2017 | Daniel Westreich | UNC Epidemiology | What is Causal Inference? |
| Friday May 19, 2017 | Bryan Lau | Johns Hopkins Epidemiology | Reflecting on the role of causal inference |
| Friday April 7, 2017 | Matt Maciejewski and Valerie Smith | Duke University and Durham Veterans Affairs Medical Center | Approaches to Longitudinal Matching |
| Friday March 3, 2017 | Alex Keil | UNC Epidemiology | Leveraging machine learning algorithms for epidemiologic inference |
| Friday February 10, 2017 | Harsha Thirumurthy | UNC Health Policy and Management | Causal effects of civil conflicts on child health |
| Friday January 13, 2017 | Brian Barkley, Sujatro Chakladar, Bradley Saul and Michael Hudgens | UNC Biostatistics | Causal Inference with Interference |
| Friday December 2, 2016 | Elizabeth Stuart | Johns Hopkins Biostatistics | Assessing and enhancing the generalizability of randomized trials to target populations |
| Thursday, November 3, 2016 | James Robins | Harvard Epidemiology | Path-specific Causal Effects |
| Friday, October 7, 2016 | Alexander Volfovsky | Duke Statistical Science | Causal inference in the presence of networks: randomization and observation |
| Friday, September 9, 2016 | Michael Hudgens | UNC Biostatistics | An Introduction to Causal Inference |
| Friday, May 6, 2016 | Doug Lauen and Fatih Unlu | UNC Public Policy, Abt Associates, Inc | Early College High Schools at Scale: Probing Impacts and Generalizability with a Quasi-Experiment Benchmarked Against an RCT |
| Friday, April 1, 2016 | Donglin Zeng | UNC Biostatistics | Personalized Medicine and SMART Designs |
| Friday, March 11, 2016 | Wen Wei Loh | University of Washington | A Finite Population Likelihood Ratio Test of the Sharp Null Hypothesis for Compliers |
| Friday, March 4, 2016 | Edward Kennedy | University of Pennsylvania Wharton School | Semiparametric Theory and Empirical Processes in Causal Inference |
| Friday, February 5, 2016 | Alex Keil | UNC Epidemiology | A Bayesian approach to the g-formula |
| Friday, January 8, 2016 | Michael Hudgens, Steve Cole and Tiffany Breger | UNC Biostatistics and Epidemiology | On the G-Null Paradox |
| Friday, December 4, 2015 | Dylan Small | University of Pennsylvania Wharton School | Dissonant Conclusions When Testing the Validity of an Instrumental Variable |
| Friday, November 6, 2015 | Daniel Westreich | UNC Epidemiology | Big Data and Causal Inference |
| Friday, October 2, 2015 | Kai Zhang | UNC Statistics and Operations Research | Using Split Samples and Evidence Factors in an Observational Study of Neonatal Outcomes |
| Friday, September 18, 2015 | Steve Cole | UNC Epidemiology | So what is “Causal Inference”? [Introductions and Roundtable Discussion] |
| Friday, May 1, 2015 | Charlie Poole | UNC Epidemiology | Public Health Interaction |
| Friday, April 17, 2015 | Peter Gilbert | Fred Hutchinson Cancer Research Center | Predicting Overall Vaccine Efficacy in a New Setting by Re-calibrating Effect Modifiers of Type-Specific Vaccine Efficacy |
| Friday, March 6, 2015 | Jeremy Moulton | UNC Public Policy | A Regression Discontinuity Analysis of Virginia Elections |
| Monday, February 23, 2015 | Caleb Miles | Harvard Biostatistics | Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program |
| Friday, February 6, 2015 | Fan Li | Duke University | Evaluating the Effect of University Grants on Student Dropout: Evidence from a Regression Discontinuity Design Using Bayesian Principal Stratification Analysis |
| Friday, January 16, 2015 | Jacob Bor | Boston University | Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials |
| Wednesday, December 3, 2014 | Joe Rigdon | UNC Biostatistics | Causal inference for binary data with interference |
| Thursday, November 20, 2014 | Miguel Hernan | Harvard Epidemiology | Comparative effectiveness of static and dynamic treatment strategies: an application of the parametric g-formula |
| Friday, October 3, 2014 | Eric Laber | NCSU Statistics | Functional feature construction for personalized treatment regimes |
| Friday, September 5, 2014 | Ashley Naimi | McGill Epidemiology | Estimating Controlled Direct Effects |
| Wednesday, April 23, 2014 | Thomas Richardson | UW Statistics | Unifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs) |
| Friday, April 4, 2014 | Eric Tchetgen Tchetgen | Harvard Biostatistics and Epidemiology | Taming selection bias in the health sciences: some new strategies |
| Friday, March 7, 2014 | Michele Jonsson-Funk | UNC Epidemiology | Treatment effect heterogeneity: What interaction terms didn't tell you |
| Friday, February 7, 2014 | Daniel Westreich and Jess Edwards | UNC Epidemiology | TBA |
| Friday, January 3, 2014 | Amy Richardson | UNC Biostatistics | Bounds and Sensitivity Analysis |
| Thursday, December 5, 2013 | Tyler VanderWeele | Harvard Epidemiology | Surrogate Measures and Consistent Surrogates |
| Friday, November 1, 2013 | Alan Brookhart | UNC Epidemiology | Risk Score Block Randomization |
| Friday, October 4, 2013 | Charlie Poole | UNC Epidemiology | Single world intervention graphs (SWIGs) |
| Friday, September 6, 2013 | Steve Cole | UNC | All of the secrets I know about risk |
| Friday, June 7, 2013 | NA | NA | Roundtable |
| Friday, May 3, 2013 | Stephen Cole | UNC | Refined risk differences and ratios in cohort studies with competing risks |
| Friday, April 5, 2013 | Wei Sun | UNC | High dimensional DAGs |
| Friday, March 1, 2013 | Michael Hudgens | UNC | Interference |
| Friday, February 1, 2013 | Susan Gruber | Harvard | Targeted maximum likelihood |
| Friday, January 4, 2013 | NA | NA | Roundtable |
| Friday, December 7, 2012 | Butch Tsiatis | NCSU | A robust method for estimating optimal treatment regimes |
| Friday, November 2, 2012 | Lauren Cain | Harvard | When to start? A systematic approach to the comparison of dynamic regimes using observational data Methods Clinical |
| Friday, October 5, 2012 | Andrew Allen | Duke | Control for Confounding in Case-Control Studies Using the Stratification Score, a Retrospective Balancing Score |
| Friday, September 7, 2012 | Jess Edwards and Alex Keil | UNC Department of Epidemiology | Comparison of three causal models to control time-varying confounding in a cohort of bone marrow transplant recipients |
| Friday, June 8, 2012 | Daniel Westreich | Dept. of Obstetrics & Gynecology, | |
| Friday, May 4, 2012 | Miguel Hernan | Harvard Department of Epidemiology | A Discussion of when to start therapy with respect to biomarkers: the case of HIV: Lancet, NEJM, and AIM. |
| Thursday, March 29, 2012 | Rhian Daniel | The London School of Hygiene and Tropical Medicine | Using causal diagrams to guide analysis in missing data problems |
| Friday, March 2, 2012 | Jay Kaufman | McGill Department of Epidemiology, Biostatistics and Occupational Health | Non-Collapsibility of Odds Ratios: Comparison of Marginal with Conditional Estimates from Logistic Regression Models |
| Friday, February 3, 2012 | Tyler VanderWeele | Harvard Department of Epidemiology | What is Confounding? |
| Friday, December 2, 2011 | Eric Tchetgen Tchetgen | Harvard Department of Epidemiology | A General Class of Methods for Causal Mediation Analysis: identification, efficiency, multiple robustness and sensitivity analysis |
| Friday, November 4, 2011 | Fan Li | Duke University Department of Statistical Science | A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference |
| Friday, October 7, 2011 | Sam Field & Matt McBee | UNC: Frank Porter Graham Child Development Institute & East Tennessee State University: Psychology | Finite Sample Bias in Propensity Score Matching/Weighting |
| Friday, September 2, 2011 | Stephen Cole | UNC: Epidemiology | Causal Inference |
| Friday, June 3, 2011 | NA | NA | Round table |
| Friday, May 6, 2011 | Dustin Long | UNC: Biostatistics | Comparing Competing Risks Outcomes Within Principal Strata |
| Friday, April 1, 2011 | Whitney Robinson | UNC | A causal approach to health disparities research |
| Friday, March 4, 2011 | M. Elizabeth Halloran | Univ. Of Washington | Causal inference for vaccine effects for infectiousness |
| Friday, February 4, 2011 | Jason Fine | UNC: Biostatistics | Sensitivity testing for nonidentifiable models |
| Thursday, January 6, 2011 | Charles Poole | UNC: Epidemiology | Effect Modification in Sufficient-Cause and Potential-Risk Frameworks |
| Friday, December 3, 2010 | Stephen Cole | UNC: Epidemiology | MSM Case cohort studies |
| Wednesday, November 3, 2010 | James Robins | Harvard | Direct and indirect effects |
| Friday, October 1, 2010 | Michael Kosorok | UNC: Biostatistics | Personalized Medicine and Clinical Trials |
| Friday, September 10, 2010 | E. Michael Foster | UNC | Generalized Methods of Moments and Causal Inference |
| Friday, June 4, 2010 | Tracy Nolen | UNC: Biostatistics | Randomization-based inference in principal strata |
| Friday, May 14, 2010 | Haitao Chu | UNC: Biostatistics | Measurement error |
| Friday, April 9, 2010 | Tyler Vanderweele | Harvard Department of Epidemiology | Mediation and interference |
| Friday, March 5, 2010 | Alan Brookhart | Bias inflation/IV | |
| Friday, February 5, 2010 | Chanelle Howe | UNC: Epidemiology | MSM for alcohol/HIV |
| Friday, January 8, 2010 | Daniel Westreich | Parametric G formula | |
| Friday, December 4, 2009 | Stephen Cole | UNC: Epidemiology | RCTs and generalizability |
| Friday, November 6, 2009 | Bryan Shepherd | Vanderbilt | When to start HAART? |
| Friday, October 2, 2009 | Kenneth Bollen | Structural equations | |
| Friday, September 4, 2009 | Daniel Almirall | Time varying effect mod. | |
| Friday, August 7, 2009 | E. Michael Foster | UNC | Instrumental variables |
| Friday, July 3, 2009 | Stephen Cole | UNC: Epidemiology | MSM with measurement-error |
| Friday, June 5, 2009 | NA | NA | Round table |
| Friday, May 1, 2009 | Michele Funk | Doubly robust inference | |
| Friday, April 3, 2009 | NA | NA | Round table |
| Friday, March 6, 2009 | E. Michael Foster | UNC | Heckman’s model |
| Friday, February 6, 2009 | Charles Poole | UNC: Epidemiology | What is gender bias? |
| Tuesday, January 6, 2009 | Stephen Cole | UNC: Epidemiology | Direct/Indirect effects |
| Tuesday, December 2, 2008 | Til Sturmer | Nurses Health Study | |
| Tuesday, November 4, 2008 | E. Michael Foster | UNC | Causal diagrams |
| Tuesday, October 7, 2008 | Michael Hudgens | UNC: Biostatistics | Principal stratification |
| Tuesday, September 2, 2008 | Stephen Cole | UNC: Epidemiology | Positivity |
| Thursday, August 7, 2008 | Michael Hudgens | UNC: Biostatistics | Interference |
| Tuesday, July 22, 2008 | Stephen Cole | UNC: Epidemiology | Consistency |
| Tuesday, July 8, 2008 | Stephen Cole | UNC: Epidemiology | Potential outcomes |