The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
Open accessAbstract
Summary: There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pretreatment variables. The unconfoundedness assumption is often more plausible if a large number of pretreatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation. We develop a method for debiasing penalized regression adjustments to allow sparse regression methods like the lasso to be used for √n-consistent inference of average treatment effects in high dimensional linear models. Given linearity, we do not need to assume that the treatment propensities are estimable, or that the average treatment effect is a sparse contrast of the outcome model parameters. Rather, in addition to standard assumptions used to make lasso regression on the outcome model consistent under 1-norm error, we require only overlap, i.e. that the propensity score be uniformly bounded away from 0 and 1. Procedurally, our method combines balancing weights with a regularized regression adjustment.
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Citations
Cites
Regression shrinkage and selection via the lasso
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Self-citationCalibration Estimators in Survey Sampling
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For objective causal inference, design trumps analysis
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The Asymptotic Variance of Semiparametric Estimators
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Stable weights that balance covariates for estimation with incomplete outcome data
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Inverse probability tilting for moment condition models with missing data
Graham, B. S., De Xavier Pinto, C. C., & Egel, D. (2012) · The Review of Economic Studies · doi:10.1093/restud/rdr047 · Cited by 174 · cited 7×
“Similar estimators have been considered by Graham et al. (2012, 2016) and Hainmueller (2012) in a setting where exact balancing is possible, with slightly different objection functions.”
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Cited by
Synthetic Difference-in-Differences
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021) · American Economic Review · doi:10.1257/aer.20190159 · Cited by 1,518
Self-citationDouble/debiased machine learning for treatment and structural parameters
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The Augmented Synthetic Control Method
Ben-Michael, E., Feller, A., & Rothstein, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1929245 · Cited by 462
“See Athey, Imbens, and Wager (2018) for analogous results on balancing weights in high dimensional cross-sectional settings.”
BackgroundA Survey of Learning Causality with Data: Problems and Methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2021) · ACM Computing Surveys · doi:10.1145/3397269 · Cited by 362
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019) · Marketing Science · doi:10.1287/mksc.2018.1135 · Cited by 287
Metadata
- Authors
- Athey, S., Imbens, G. W., & Wager, S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-02-15
- Volume
- 80
- Issue
- 4
- Pages
- 597-623
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2565113373
- Cited by
- 311
- References
- 10
- FWCI
- 30.23
- Type
- journal article
- Language
- en
- Open access
- green
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