Session 137:
Causal Inference and Experimental Designs
Chair: Deirdre Bloome, Harvard University
Discussant: Liying Luo, University of Minnesota
Fostering the Use of Quasi-Experimental Designs for Evaluating Public Health Interventions: Insights from an MNCH mHealth Project in Malawi Jean 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.
Dynamic Optimization in Models for State Panel Data: A Cohort Panel Data Model for the Effects of Divorce Laws on Divorce Rates Tongyai Iyavarakul, Office of the Prime Minister, Thailand ; Marjorie McElroy, Duke University ; Kalina Staub, University of Toronto, Mississauga
Estimating the Effect of Fertility on Poverty in Vietnam: An Example of Causal Inference with Multilevel Data in Demographic Research Bruno Arpino, Universitat Pompeu Fabra
An Experimental Framework for Continual Improvement in Survey Research Dennis Feehan, Princeton University ; Matthew J. Salganik, Princeton University
Other sessions on Data and Methods