Friday, May 2 / 1:00 PM - 2:30 PM   •   Clarendon/Dartmouth

Session 137:
Causal Inference and Experimental Designs

Chair: Deirdre Bloome, Harvard University
Discussant: Liying Luo, University of Minnesota

  1. Fostering the Use of Quasi-Experimental Designs for Evaluating Public Health Interventions: Insights from an MNCH mHealth Project in MalawiJean Christophe Fotso, Concern Worldwide U.S., Inc. ; Jessica Crawford, VillageReach ; A. Camielle Noordam, United Nations Children's Fund (UNICEF) ; Zachariah Jezman, VillageReach ; Ariel Higgins-Steele, Concern Worldwide U.S., Inc. ; Amanda Robinson, Ohio State University ; Hastings Honde, Invest in Knowledge Initiative (IKI) ; Mila Rosenthal, Concern Worldwide U.S., Inc.

  2. Dynamic Optimization in Models for State Panel Data: A Cohort Panel Data Model for the Effects of Divorce Laws on Divorce RatesTongyai Iyavarakul, Office of the Prime Minister, Thailand ; Marjorie McElroy, Duke University ; Kalina Staub, University of Toronto, Mississauga

  3. Estimating the Effect of Fertility on Poverty in Vietnam: An Example of Causal Inference with Multilevel Data in Demographic ResearchBruno Arpino, Universitat Pompeu Fabra

  4. An Experimental Framework for Continual Improvement in Survey ResearchDennis Feehan, Princeton University ; Matthew J. Salganik, Princeton University

Other sessions on Data and Methods