Below is the list of acceptedÌýposter presenters with titles and numbered locations on the posterboards. Ìý
The poster session will be held on Thursday eveningÌýfrom 5:00 pm to 7:00 pm in the New Residence Hall, Room B. Ìý The list of poster presenters and titles can be found below with their corresponding poster information.Ìý
Presenters will be able to set their posters up during the lunch break on Thursday. Ìý
Ìý
| Poster Number | Name | Title |
|---|---|---|
| 1 | Meg Fluharty | Educational differentials in domain specific physical activity by ethnicity, age, and gender: findings from Understanding Society, The UK Household Longitudinal Study. |
| 2 | Eli Sherman | Friendship Interventions |
| 3 | Peter Lucas Cohen | Bootstrap prepivoting for finite population causal inference |
| 4 | Jiaxi Yang | Small sample criterion for covariate balance in rerandomization |
| 5 | Brian L. Egleston | Using Summary Comorbidity Measures for Prognosis and Survival Treatment Effect Estimation |
| 6 | Nicholas J Parr | Latent Class Analysis with Inverse Propensity Weighting: Assessing the Causal Relation of University-setting Sexual Assault with Depression, Anxiety, Substance Use, and Loss of Control Eating |
| 7 | Harsh Parikh | MALTS: Matching After Learning to Stretch |
| 8 | Nadia Sourial | How robustly do we verify the assumptions of the causal inference framework? Qualitative methods can provide a more in-depth and informed assessment |
| 9 | Rebecca Taylor | Studying the effect of ship disturbance on walrus behavior: an uncommon use of causal inference techniques |
| 10 | Spencer Woody | Characterizing treatment effect heterogeneity via posterior summarization |
| 11 | Kuan Liu | Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework |
| 12 | Mariel Finucane | Beyond Treatment Versus Control: How Bayesian Hierarchical Models Make Factorial Experiments Feasible in Education Research |
| 13 | Etsuji Suzuki | Causal diagrams for propensity score methods |
| 14 | Naoki Egami | Identification of Causal Diffusion Effects Using Stationary Causal Directed Acyclic Graphs |
| 15 | Thomas Leavitt | Design-Based Bayesian Inference for Causal Effects |
| 16 | Chen Lu | Non-Parametric Regression Adjustments for Difference-in-Differences Estimation |
| 17 | Evan Rosenman | Designing Experiments to Complement Observational Studies |
| 18 | Bikram Karmakar | Effect of Medical Marijuana Laws on Traffic Fatalities: An Evidence Factors Analysis in a Difference-in-Differences Study |
| 19 | Shuangning Li | Balancing Covariates in Regression Discontinuity Designs |
| 20 | Tianyang Zhang | Comparing methods for estimation of heterogeneous treatment effects using observational data from education databases |
| 21 | Noemi | Exploring heterogeneous treatment effects to inform the targeting of national health insurance programmes |
| 22 | Jiongyi Cao | Discovering Causal Heterogeneity in Medicaid Utilization with a Tree-Based Machine Learning Method |
| 23 | Lucy Mosquera | Comparing the Performance of Instrumental Variable Methods When Estimating the Causal Effect of Treatment In Pragmatic Trials with Non-Compliance |
| 24 | Jiaqing Zhang | A novel instrumental variable approach to estimate nested treatment heterogeneity |
| 25 | Sheng Wang | Weak-Instrument Robust Estimators and Tests for Two-Sample Summary Mendelian Randomization |
| 26 | Matthew Tudball | An interval estimation approach for selection bias in OLS and IV studies |
| 27 | Kathleen Kennedy-Turner | Using causal mediation based on counterfactuals in longitudinal analyses for child development: Two data case studies |
| 28 | Christiane Didden | Counterfactual-based mediation analysis in the social sciences.: An analysis of the gender pay gap. |
| 29 | Oliver Hines | A new test for an indirect effect in the confounded single mediator problem using doubly robust G-estimation |
| 30 | Nima Hejazi | Nonparametric-efficient causal mediation analysis for stochastic interventions |
| 31 | Shuxi Zeng | Causal Mediation Analysis for Sparse and Irregular Longitudinal Data |
| 32 | Andrew Ying | Causal Effects on Birth Defects with Missing by Terathanasia |
| 33 | Jaron Lee | Computationally Efficient Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes |
| 34 | Falco Joannes Bargagli Stoffi | Heterogeneous causal effects with imperfect compliance. A novel Bayesian machine learning approach |
| 35 | Lina Montoya | Performance of Super-Learner Based Optimal Dynamic Rule Estimation |
| 36 | Ranjani Srinivasan | Pathway Dependent Causal Models |
| 37 | Eli Ben-Michael | Causal inference with simple models and complex features |
| 38 | Edward Wu | The P-LOOP Estimator: Covariate Adjustment for Paired Experiments |
| 39 | Ismaila Balde | Generalized Outcome Adaptive Lasso: Variable Selection for High Dimensional Causal Inference |
| 40 | Razieh Nabi | Learning Optimal Fair Policies |
| 41 | Leah Comment | Nonparametric causal inference for semicompeting risks using Bayesian Additive Regression Trees (BART) |
| 42 | Kevin P. Josey | A Framework for Covariate Balance using Bregman Distances |
| 43 | Amelia J. Averitt | Adversarial training to learn feature-balancing weights for cohorts: The Counterfactual Chi-GAN |
| 44 | Tyrel Stokes | Model Selection under Unmeasured Confounding: Understanding the role of Bias Amplification |
| 45 | Karthik Rajkumar | Ridge regularization for Mean Squared Error Stabilization in Regression with Weak Instruments |
| 46 | Karthik Rajkumar | Exact p-values and partial identification of treatment effects in a regression discontinuity design with manipulation |
| 47 | Huaqing Zhao | Principal Components and Propensity Scores (PCAPS): Adjustment for Confounding in High-dimensional Observational Studies |
| 48 | Michele Santacatterina | Optimal balancing of time-dependent confounders for marginal structural models |
| 49 | Rui Lu | Two-step BART: a generalized framework to estimate average treatment effects when treatment is a latent class |
| 50 | Shirley Liao | Bayesian additive regression trees for confounder selection in high-dimentional settings |
| 51 | Yue You | Application of targeted learning to assess the performance of a diabetes care program, in terms of quality of care and health outcomes |
| 52 | Michael Schomaker | Time-dependent causal dose‰ÛÒresponse curves under limited data support ‰ÛÒ An example from HIV treatment research |
| 53 | Ted Westling | Causal Isotonic Regression |
| 54 | Ruoqi Yu | Matching Methods for Observational Studies Derived from Large Administrative Databases |
| 55 | Drew Dimmery | Permutation Weighting |
| 56 | Shu Yang | Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations |
| 57 | Alexander D’Amour | On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives |
| 58 | Yan Leng | Observational causal inference using network information |
| 59 | Yan Leng | Estimating social influence with homophilous latent positions |
| 60 | Jose Itamar Mendes de Souza Junior | Causal Inference and Ad Hoc Social Networks for Health Professional Societies |
| 61 | Rohit Bhattacharya | Causal Inference Under Interference and Network Uncertainty |
| 62 | Rohit Bhattacharya | Identification In Missing Data Models Represented By Directed Acyclic Graphs |
| 63 | Sebastian Martinez | Causal Inferential Dynamic Network Analysis for Public Health: STASH |
| 64 | Zahide Alaca | Regression-with-residuals estimation of marginal effects: A method of adjusting for treatment-induced confounders that may also be effect modifiers |
| 65 | Michael Lingzhi Li | Experimental Evaluation of Individualized Treatment Rules |
| 66 | Hao Sun | On estimation of optimal treatment regimes for maximizing quality adjusted lifetime |
| 67 | Jonathan Stiles | Inference For the Smoothed Proportion Whose Average Treatment Exceeds a Threshold |
| 68 | Alexander Franks | Latent Confounder Approaches to Sensitivity without Observable Implications |
| 69 | Aurelien Bibaut | Randomization inference and sensitivity analysis in two-stage observational studies with interference |
| 70 | Siyu Heng | Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach |
| 71 | Matteo Bonvini | Sensitivity Analysis via the Proportion of Unmeasured Confounding |
| 72 | David Choi | Constrasts attributable to treatment: what can be learned with no assumptions on interference? |
| 73 | James Bisbee | Non-Parametric Synthetic Controls via Implicit Randomization |
| 74 | Alan Mishler |
Counterfactual Fairness in the Actual World |
| 74 | Amanda Coston |
Doubly-robust algorithmic risk assessment |