Abadie, A.: Semiparametric difference-in-differences estimators. Rev. Econ. Stud. 72(1), 1–19 (2005)
Angrist, J.D., Pischke, J.S.: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton (2009)
Beaumont, J.F., Charest, A.S.: Bootstrap variance estimation with survey data when estimating model parameters. Comput. Stat. Data Anal. 56(12), 4450–4461 (2012)
Cawley, J., Frisvold, D., Hill, A., Jones, D.: The impact of the philadelphia beverage tax on prices and product availability. J. Policy Anal. Manage. 39(3), 605–628 (2020)
Cerdá, M., Wall, M., Feng, T., Keyes, K.M., Sarvet, A., Schulenberg, J., O’malley, P.M., Pacula, R.L., Galea, S., Hasin, D.S.: Association of state recreational Marijuana laws with adolescent marijuana use. JAMA Pediatr. 171(2), 142–149 (2017)
Article PubMed PubMed Central Google Scholar
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., Robins, J.: Double/debiased machine learning for treatment and structural parameters. Econom. J. 21(1), C1–C68 (2018)
Dong, N., Stuart, E.A., Lenis, D., Quynh Nguyen, T.: Using propensity score analysis of survey data to estimate population average treatment effects: a case study comparing different methods. Eval. Rev. 44(1), 84–108 (2020)
Dwomoh, D., Agyabeng, K., Agbeshie, K., Incoom, G., Nortey, P., Yawson, A., Bosomprah, S.: Impact evaluation of the free maternal healthcare policy on the risk of neonatal and infant deaths in four sub-Saharan African countries: a quasi-experimental design with propensity score kernel matching and difference in differences analysis. BMJ Open 10(5), e033356 (2020)
Article PubMed PubMed Central Google Scholar
Edmondson, E.K., Roberto, C.A., Gregory, E.F., Mitra, N., Virudachalam, S.: Association of a sweetened beverage tax with soda consumption in high school students. JAMA Pediatr. 175(12), 1261–1268 (2021)
Gibson, L., Zimmerman, F.: Measuring the sensitivity of difference-in-difference estimates to the parallel trends assumption. Res. Methods Med. Health Sci. 2(4), 148–156 (2021)
Han, B., Yu, H., Friedberg, M.W.: Evaluating the impact of parent-reported medical home status on children’s health care utilization, expenditures, and quality: A difference-in-differences analysis with causal inference methods. Health Serv. Res. 52(2), 786–806 (2017)
Hirano, K., Imbens, G.W., Ridder, G.: Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71(4), 1161–1189 (2003)
Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986)
Hong, S.H.: Measuring the effect of napster on recorded music sales: difference-in-differences estimates under compositional changes. J. Appl. Economet. 28(2), 297–324 (2013)
Howe, K.B., Suharlim, C., Ueda, P., Howe, D., Kawachi, I., Rimm, E.B.: Gotta catch’em all! pokémon go and physical activity among young adults: difference in differences study. BMJ (2016). https://doi.org/10.1136/bmj.i6270
Article PubMed PubMed Central Google Scholar
Imai, K., Kim, I.S.: When should we use unit fixed effects regression models for causal inference with longitudinal data? Am. J. Political Sci. 63(2), 467–490 (2019)
Kann, L.: Youth risk behavior surveillance—United States, 2017. Surv. Summaries 67(8), 1–114 (2018)
Kim, J.K., Rao, J., Wang, Z.: Hypotheses testing from complex survey data using bootstrap weights: a unified approach. J. Am. Stat. Assoc. 119(546), 1229–1239 (2024)
Klootwijk, A., Struijs, J., Petrus, A., Leemhuis, M., Numans, M., de Vries, E.: Do studies evaluating early-life policy interventions fully adhere to the critical conditions of difference-in-differences? a systematic review. BMJ Open 14(5), e083927 (2024)
Article PubMed PubMed Central Google Scholar
Kosova, E.C., Auinger, P., Bremer, A.A.: The relationships between sugar-sweetened beverage intake and cardiometabolic markers in young children. J. Acad. Nutr. Diet. 113(2), 219–227 (2013)
Article PubMed PubMed Central Google Scholar
Kostouraki, A., Hajage, D., Rachet, B., Williamson, E.J., Chauvet, G., Belot, A., Leyrat, C.: On variance estimation of the inverse probability-of-treatment weighting estimator: a tutorial for different types of propensity score weights. Stat. Med. 43(13), 2672–2694 (2024)
Malik, V.S., Pan, A., Willett, W.C., Hu, F.B.: Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am. J. Clin. Nutr. 98(4), 1084–1102 (2013)
Article PubMed PubMed Central Google Scholar
Miao, W., Li, X., Zhang, P., Sun, B.: A stableness of resistance model for nonresponse adjustment with callback data. J. R. Stat. Soc. Ser. B Stat. Methodol. 87(2), 433–456 (2025)
Miller, G.F., Sliwa, S., Brener, N.D., Park, S., Merlo, C.L.: School district policies and adolescents’ soda consumption. J. Adolesc. Health 59(1), 17–23 (2016)
Article PubMed PubMed Central Google Scholar
Oduse, S., Zewotir, T., North, D.: The impact of antenatal care on under-five mortality in ethiopia: a difference-in-differences analysis. BMC Pregnancy Childbirth 21, 1–9 (2021)
Park, S., Sherry, B., Foti, K., Blanck, H.M.: Self-reported academic grades and other correlates of sugar-sweetened soda intake among us adolescents. J. Acad. Nutr. Diet. 112(1), 125–131 (2012)
Powell, L.M., Leider, J.: The impact of Seattle’s sweetened beverage tax on beverage prices and volume sold. Econ. Hum. Biol. 37, 100856 (2020)
Rao, M., Katyal, A., Singh, P.V., Samarth, A., Bergkvist, S., Kancharla, M., Wagstaff, A., Netuveli, G., Renton, A.: Changes in addressing inequalities in access to hospital care in andhra pradesh and maharashtra states of india: a difference-in-differences study using repeated cross-sectional surveys. BMJ Open 4(6), e004471 (2014)
Article PubMed PubMed Central Google Scholar
Reedy, J., Krebs-Smith, S.M.: Dietary sources of energy, solid fats, and added sugars among children and adolescents in the united states. J. Am. Diet. Assoc. 110(10), 1477–1484 (2010)
Article PubMed PubMed Central Google Scholar
Ridgeway, G., Kovalchik, S.A., Griffin, B.A., Kabeto, M.U.: Propensity score analysis with survey weighted data. J. Causal Inference 3(2), 237–249 (2015)
Article PubMed PubMed Central Google Scholar
Roberto, C.A., Lawman, H.G., LeVasseur, M.T., Mitra, N., Peterhans, A., Herring, B., Bleich, S.N.: Association of a beverage tax on sugar-sweetened and artificially sweetened beverages with changes in beverage prices and sales at chain retailers in a large urban setting. JAMA 321(18), 1799–1810 (2019)
Article PubMed PubMed Central Google Scholar
Roth, J.: Pretest with caution: event-study estimates after testing for parallel trends. Am. Econ. Rev. Insights 4(3), 305–322 (2022)
Rust, K.F., Rao, J.: Variance estimation for complex surveys using replication techniques. Stat. Methods Med. Res. 5(3), 283–310 (1996)
Ryan, A.M., Burgess, J.F., Jr., Dimick, J.B.: Why we should not be indifferent to specification choices for difference-in-differences. Health Serv. Res. 50(4), 1211–1235 (2015)
Sant’Anna, P.H., Xu, Q.: Difference-in-differences with compositional changes. arXiv preprint (2023). arXiv:2304.13925
Stuart, E.A., Huskamp, H.A., Duckworth, K., Simmons, J., Song, Z., Chernew, M.E., Barry, C.L.: Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health Serv. Outcomes Res. Method. 14, 166–182 (2014)
Su, D., Chen, Y.C., Gao, H.X., Li, H.M., Chang, J.J., Jiang, D., Hu, X.M., Lei, S.H., Tan, M., Chen, Z.F.: Effect of integrated urban and rural residents medical insurance on the utilisation of medical services by residents in china: a propensity score matching with difference-in-differences regression approach. BMJ Open 9(2), e026408 (2019)
Article PubMed PubMed Central Google Scholar
Su, Q., Wang, H., Fan, L.: The impact of home and community care services pilot program on healthy aging: a difference-in-difference with propensity score matching analysis from china. Arch. Gerontol. Geriatr. 110, 104970 (2023)
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