Andrés Ramírez-Hassan, Raquel Vargas-Correa, Gustavo García, Daniel Londoño
arXiv 14 Aug 2020 · Econometrics
arXiv:2008.06564 · PDF · DOI · OpenAlex · Extracted main text
We propose a simple approach to optimally select the number of control units in k nearest neighbors (kNN) algorithm focusing in minimizing the mean squared error for the average treatment effects. Our approach is non-parametric where confidence intervals for the treatment effects were calculated using asymptotic results with bias correction. Simulation exercises show that our approach gets relative small mean squared errors, and a balance between confidence intervals length and type I error. We analyzed the average treatment effects on treated (ATET) of participation in 401(k) plans on accumulated net financial assets confirming significant effects on amount and positive probability of net asset. Our optimal k selection produces significant narrower ATET confidence intervals compared with common practice of using k=1.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, S., Imbens, G., and Ramachandra, V (2015) Machine learning methods for estimating heterogeneous causal effects | 0.843 | 4 | 3 | 75% |
| 2 | Abadie, A. and Imbens, G (2006) Large sample properties of matching estimators for average treatment effects | 0.811 | 5 | 2 | 80% |
| 3 | Hastie, T., Tibshirani, R., and Friedman, J (2009) The Elements of Statistical Learning Data Mining, Inference, and Prediction | 0.737 | 3 | 2 | 100% |
| 4 | Abadie, A. and Imbens, G (2016) Matching on the estimated propensity score | 0.644 | 2 | 2 | 100% |
| 5 | Cameron, C. and Trivedi, K (2005) Microeconometrics: Methods and Applications | 0.644 | 2 | 2 | 100% |
| 6 | Otsu, T. and Rai, Y (2017) Bootstrap inference of matching estimators for average treatment effects | 0.585 | 3 | 1 | 100% |
| 7 | Rosenbaum, P. R. and Rubin, D. B (1983) The central role of the propensity score in observational studies for causal effects | 0.585 | 3 | 1 | 100% |
| 8 | Abadie, A. and Imbens, G (2011) Bias-corrected matching estimators for average treatment effects | 0.511 | 2 | 2 | 50% |
| 9 | Benjamin, D (2003) Does 401(k) eligibility increase savings? Evidence from propensity score subclassification | 0.511 | 2 | 1 | 100% |
| 10 | Conley, T., Hansen, C., and Rossi, P (2012) Plausibly exogenous | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 15 scored citations.