Acharya, A., Blackwell, M., Sen, M. (2016). Explaining causal findings without bias: Detecting and assessing direct effects. American Political Science Review, 110(3), 512-529.
Angrist, J. D., Fernandez-Val, I. (2013). ExtrapoLATE-ing: External validity and overidentification in the LATE framework. In: Acemoglu, D., Arellano, M., Dekel, E. (eds.). Advances in Economics and Econometrics, Tenth World Congress, Volume III, Econometrics, Chapter 11.
- Angrist, J. D., Imbens, G. W., Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455.
Paper not yet in RePEc: Add citation now
Aronow, P.M., Carnegie, A. (2013). Beyond LATE: Estimation of the average treatment effect with an instrumental variable. Political Analysis, 21, 492-506 Binswanger, H.P. (1980). Attitudes toward risk: Experimental measurement in rural India. American Journal of Agricultural Economics, 62, 395-407.
Busso, M., DiNardo, J., McCrary, J. (2014). New evidence on the finite sample properties of propensity score reweighting and matching estimators. Review of Economics and Statistics, 96(5), 885-897.
- Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 215-242.
Paper not yet in RePEc: Add citation now
Dal Bó, P., Foster, A., Putterman, L. (2010). Institutions and behavior: Experimental evidence on the effects of democracy. American Economic Review, 100(5), 2205-2229.
Deuchert, E., Huber, M., Schelker, M. (2018). Direct and indirect effects based on difference-in-differences with an application to political preferences following the Vietnam draft lottery. Journal of Business and Economic Statistics, DOI: 10.1080/07350015.2017.1419139.
Deuchert, E., Wunsch, C. (2014). Evaluating nationwide health interventions: Malawi’s insecticidetreated -net distribution programme. Journal of the Royal Statistical Society: Series A, 177(2), 523-552.
Eckel, C., Grossman, P. (2002). Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior, 23(4), 281–295.
Fischbacher, U. (2007). Z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10, 171-178.
Flores, C. A., Flores-Lagunes, A. (2009). Identification and estimation of causal mechanisms and net effects of a treatment under unconfoundedness. IZA Dicussion Paper No. 4237.
- For the direct effect we have δ1−t = θ0 Y − γt where θ0 Y = E[Yi∣Di = 1] − E[Yi∣Di = 0] in the treatment equivalence design and θ0 Y = E[Yi∣Ti = 1,Di = 0] − E[Yi∣Ti = 0,Di = 0] in all other designs. ∎ A.3 The risk preference game In the game, each subject was asked to choose one out of eight different lotteries (see Table 6, columns 2 to 4). The first alternative offers a certain amount of 320 Kenyan Shillings. The subsequent lotteries yield either a high (HEADS) or a low (TAILS) payoff with probability .5. While the first six lotteries are increasing in expected values and variances of payoffs, the last lottery R has the same expected payoff as Q, but implies a higher variance. Hence, only risk-neutral or risk-loving subjects should choose this dominated gamble (Binswanger, 1980).
Paper not yet in RePEc: Add citation now
Frölich, M., Huber, M. (2017). Direct and indirect treatment effects - causal chains and mediation analysis with instrumental variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(5), 1645-1666.
Giné, X., Jakiela, P., Karlan, D., Morduch, J. (2010). Microfinance games. American Economic Journal: Applied Economics, 2(3), 60-95.
Gollier, C., Pratt, J.W. (1996). Risk vulnerability and the tempering effect of background risk. Econometrica, 64(5), 1109-1123.
Guiso, L., Sapienza, P., Zingales, L. (2004). The role of social capital in financial development. American Economic Review, 94(3), 526-556.
Harrison, G.W. Humphrey, S.J., Verschoor A. (2010). Choice under uncertainty: Evidence from Ethiopia, India and Uganda. Economic Journal, 120(543), 80–104.
- Haushofer, J., Collins, M., de Giusti, B., Njoroge, J.M., Odero, A., Onyango, C., Vancel, J., Jang, C, Kuruvilla, M.V., Hughes, C. (2014). A methodology for laboratory experiments in developing countries: Examples from the Busara Center. Unpublished manuscript.
Paper not yet in RePEc: Add citation now
- Hong, G. (2010). Ratio of mediator probability weighting for estimating natural direct and indirect effects. In: Proceedings of the American Statistical Association, Biometrics Section (pp. 2401-2415). Alexandria, VA: American Statistical Association.
Paper not yet in RePEc: Add citation now
Hsu, Y., Huber, M., Lai, T.C. (2017). Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting. Working Paper SES No. 482, University of Friburg.
Huber, M. (2014). Identifying causal mechanisms (primarily) based on inverse probability weighting. Journal of Applied Econometrics, 29(6), 920-943.
Huber, M., Lechner, M., Mellace, G. (2017). Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism. Review of Economics and Statistics, 99(1), 180-183.
Huber, M., Lechner, M., Wunsch, C. (2013). The performance of estimators based on the propensity score. Journal of Econometrics, 175(1), 1-21.
Imai, K., Keele, L., Tingley, D., Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105(4), 765-789.
- Imai, K., Keele, L., Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51-71.
Paper not yet in RePEc: Add citation now
Imai, K., Tingley, D., Yamamoto, T. (2013). Experimental designs for identifying causal mechanisms. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(1), 5-51.
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4-29.
Janevic, M.R., Janz, N.K., Dodge, J.A., Lin, X., Pan, W., Sinco, B.R., Clark, N.M.(2003). The role of choice in health education intervention trials: a review and case study. Social Science and Medicine, 56, 1581-1594.
Joyce, T., Remler, D.K., Jaeger, D.A., Altindag, O., O’Connell, S.D., Crockett, S. (2017). On measuring and reducing selection bias with a quasi-doubly randomized preference trial. Journal of Policy Analysis and Management, 36(2), 438.459.
Karlan, D. S. (2005). Using experimental economics to measure social capital and predict financial decisions.
Karlan, D.S., Zinman, J. (2009). Observing unobservables: Identifying information asymmetries with a consumer credit field experiment. Econometrica, 77(6), 1993-2008.
- Knaus, M., Lechner, M., Strittmatter, A. (2017). Uncovering Treatment Effect Heterogeneity in Swiss Job Search Programs. IZA Discussion Paper No. 10961.
Paper not yet in RePEc: Add citation now
Lechner, M. (1995). Some Specification Tests for Probit Models Estimated on Panel Data. Journal of Business and Economic Statistics, 13 (4), 475-488.
Long, Q., Little, R.J., Lin, X. (2008). Causal inference in hybrid intervention trials involving treatment choice. Journal of the American Statistical Association, 103(482), 474-484.
- Marcus, S.M., Stuart, E.A., Wang, P., Shadish, W.R., Steiner, P.M. (2012). Estimating the causal effect of randomization versus treatment preference in a doubly randomized preference trial. Psychological Methods, 17(2), 244-254.
Paper not yet in RePEc: Add citation now
Narayan, D., Pritchett, L. (1999). Cents and sociability: Household income and social capital in rural Tanzania. Economic Development and Cultural Change, 47(4), 871-897.
Newey, W. K. (1984). A method of moments interpretation of sequential estimators. Economics Letters, 14(2-3), 201-206.
- Park, S. (2015). Abstract: Identifying average causal mediation effects with multiple mediators in the presence of treatment noncompliance. Multivariate Behavioral Research, 50(1), 141-141.
Paper not yet in RePEc: Add citation now
- Pearl, J. (2001). Direct and indirect effects. In: Breese, J.S., Koller, D. (Eds.). Proceedings of the 17th conference on uncertainty in artificial intelligence (pp. 411-420). San Francisco: Morgan Kaufmann.
Paper not yet in RePEc: Add citation now
- Petersen, M. L., Sinisi, S. E., van der Laan, M. J. (2006). Estimation of direct causal effects. Epidemiology, 17(3), 276-284.
Paper not yet in RePEc: Add citation now
- Pirlott, A. G., MacKinnon, D. P. (2016). Design approaches to experimental mediation. Journal of Experimental Social Psychology, 66, 29-38.
Paper not yet in RePEc: Add citation now
- Rücker, G. (1989). A two-stage trial design for testing treatment, self-selection and treatment preference effects. Statistics in Medicine, 8(4), 477-485.
Paper not yet in RePEc: Add citation now
- Robins, J. M. (2003). Semantics of causal DAG models and the identification of direct and indirect effects. In: Green, P., Hjort, N., Richardson, S. (Eds.). Highly Structured Stochastic Systems (pp. 70-81). Oxford: Oxford University Press.
Paper not yet in RePEc: Add citation now
- Robins, J. M., Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 143-155.
Paper not yet in RePEc: Add citation now
- Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31(2), 161-170.
Paper not yet in RePEc: Add citation now
- Selten, R. (1967). Die Strategiemethode zur Erforschung des eingeschrankt rationalen Verhaltens im Rahmen eines Oligopolexperimentes. In: Sauermann, H. (Ed.). Beitrage zur experimentellen Wirtschaftsforschung (pp. 136-168). Tübingen: Mohr.
Paper not yet in RePEc: Add citation now
Shadish, W.R., Clark, M.H., Steiner, P.M. (2008). Can nonrandomized experiments yield accurate answers ? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association, 103(484), 1334-1356.
Sloczynski, T. (2016). A general weighted average representation of the ordinary and two-stage least squares estimands. Unpublished manuscript.
Strobl, R., Wunsch, C. (2018). Does voluntary risk taking affect solidarity? Experimental evidence from Kenya. CEPR Discussion Paper No. 12996.
- Tchetgen Tchetgen, E. J., Shpitser, I. (2012). Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness, and sensitivity analysis. Annals of Statistics, 40(3), 1816-1845.
Paper not yet in RePEc: Add citation now
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.
Paper not yet in RePEc: Add citation now
- VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20(1), 18-26.
Paper not yet in RePEc: Add citation now
Wunsch, C., Strobl, R. (2018). Risky Choices and Solidarity: Why Experimental Design Matters. CEPR Discussion Paper No. 12995.
- Yamamoto, T. (2014) Identification and estimation of causal mediation effects with treatment noncompliance. Unpublished Manuscript.
Paper not yet in RePEc: Add citation now