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Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects

  • Jianing Zhao
  • Daniel M. Runfola
  • Peter Kemper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)

Abstract

The World Bank provides billions of dollars in development finance to countries across the world every year. As many projects are related to the environment, we want to understand the World Bank projects impact to forest cover. However, the global extent of these projects results in substantial heterogeneity in impacts due to geographic, cultural, and other factors. Recent research by Athey and Imbens has illustrated the potential for hybrid machine learning and causal inferential techniques which may be able to capture such heterogeneity. We apply their approach using a geolocated dataset of World Bank projects, and augment this data with satellite-retrieved characteristics of their geographic context (including temperature, precipitation, slope, distance to urban areas, and many others). We use this information in conjunction with causal tree (CT) and causal forest (CF) approaches to contrast ‘control’ and ‘treatment’ geographic locations to estimate the impact of World Bank projects on vegetative cover.

References

  1. 1.
  2. 2.
  3. 3.
    Athey, S., Imbens, G.: Recursive partitioning for heterogeneous causal effects (2015)Google Scholar
  4. 4.
    Biau, G.: Analysis of a random forests model. JMLR 13(1), 1063–1095 (2012)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Denil, M., Matheson, D., de Freitas, N.: Narrowing the gap: random forests in theory and in practice. In: ICML (2014)Google Scholar
  7. 7.
    Dunbar, B.: NDVI: satellites could help keep hungry populations fed as climate changes (2015). http://www.nasa.gov/topics/earth/features/obscure_data.html
  8. 8.
    Hirano, K., Imbens, G., Ridder, G.: Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71(4), 1161–1189 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Imbens, G.W., Rubin, D.B.: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press, Cambridge (2015)CrossRefzbMATHGoogle Scholar
  10. 10.
    Meinshausen, N.: Quantile regression forests. JMLR 7, 983–999 (2006)MathSciNetzbMATHGoogle Scholar
  11. 11.
    NASA: The land long term data record (2015). http://ltdr.nascom.nasa.gov/cgi-bin/ltdr/ltdrPage.cgi
  12. 12.
    Su, X., Tsai, C.L., Wang, H., Nickerson, D.M., Li, B.: Subgroup analysis via recursive partitioning. J. Mach. Learn. Res. 10, 141–158 (2009)Google Scholar
  13. 13.
    Taddy, M., Gardner, M., Chen, L., Draper, D.: A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation (2014)Google Scholar
  14. 14.
    Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Ser. B 58, 267–288 (1994)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, New York (1998)zbMATHGoogle Scholar
  16. 16.
    Wager, S., Athey, S.: Estimation and inference of heterogeneous treatment effects using random forests (2015)Google Scholar
  17. 17.
    Wager, S., Hastie, T., Efron, B.: Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. JMLR 15(1), 1625–1651 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jianing Zhao
    • 1
  • Daniel M. Runfola
    • 2
  • Peter Kemper
    • 1
  1. 1.College of William and MaryWilliamsburgUSA
  2. 2.AidDataWilliamsburgUSA

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