Session 137:
Causal Inference and Experimental Designs

Friday, May 2
1:00 PM - 2:30 PM
Clarendon/Dartmouth
3rd Floor

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