Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects
From MaRDI portal
Publication:5134472
zbMath1453.62431arXiv1605.00155MaRDI QIDQ5134472
Publication date: 16 November 2020
Full work available at URL: https://arxiv.org/abs/1605.00155
Related Items
Estimating causal effects with optimization-based methods: a review and empirical comparison ⋮ Hospital quality risk standardization via approximate balancing weights ⋮ Varying impacts of letters of recommendation on college admissions ⋮ Covariate balancing propensity score by tailored loss functions
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Does matching overcome LaLonde's critique of nonexperimental estimators?
- Robust inference on average treatment effects with possibly more covariates than observations
- Matching methods for causal inference: a review and a look forward
- Comment on J. Neyman and causal inference in experiments and observational studies: On the application of probability theory to agricultural experiments. Essay on principles. Section 9 [Ann. Agric. Sci. 10 (1923), 1--51]
- On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
- Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements
- Large sample confidence regions based on subsamples under minimal assumptions
- On the Failure of the Bootstrap for Matching Estimators
- The central role of the propensity score in observational studies for causal effects
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Program Evaluation and Causal Inference With High-Dimensional Data
- Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
- Generalized Optimal Matching Methods for Causal Inference
- Double/debiased machine learning for treatment and structural parameters
- Multivariate Matching Methods That Are Monotonic Imbalance Bounding
- Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
- Bias-Corrected Matching Estimators for Average Treatment Effects
- Large Sample Properties of Matching Estimators for Average Treatment Effects
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- Covariate Balancing Propensity Score
- On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known