The balancing act in causal inference
The idea of covariate balance is at the core of causal inference. Inverse propensity weights
play a central role because they are the unique set of weights that balance the covariate …
play a central role because they are the unique set of weights that balance the covariate …
Towards representation learning for weighting problems in design-based causal inference
Reweighting a distribution to minimize a distance to a target distribution is a powerful and
flexible strategy for estimating a wide range of causal effects, but can be challenging in …
flexible strategy for estimating a wide range of causal effects, but can be challenging in …
Augmented balancing weights as linear regression
We provide a novel characterization of augmented balancing weights, also known as
automatic debiased machine learning (AutoDML). These popular doubly robust or double …
automatic debiased machine learning (AutoDML). These popular doubly robust or double …
Soft calibration for selection bias problems under mixed-effects models
Calibration weighting has been widely used to correct selection biases in nonprobability
sampling, missing data and causal inference. The main idea is to calibrate the biased …
sampling, missing data and causal inference. The main idea is to calibrate the biased …
[PDF][PDF] Augmented balancing weights as undersmoothed regressions
The augmented balancing weights framework, also known as automatic debiased machine
learning, is a powerful approach to causal inference that has recently seen a flurry of …
learning, is a powerful approach to causal inference that has recently seen a flurry of …
kpop: a kernel balancing approach for reducing specification assumptions in survey weighting
With the precipitous decline in response rates, researchers and pollsters have been left with
highly nonrepresentative samples, relying on constructed weights to make these samples …
highly nonrepresentative samples, relying on constructed weights to make these samples …
Approximate balancing weights for clustered observational study designs
In a clustered observational study, a treatment is assigned to groups and all units within the
group are exposed to the treatment. We develop a new method for statistical adjustment in …
group are exposed to the treatment. We develop a new method for statistical adjustment in …
Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints
MR Williams, TD Savitsky - Journal of Computational and …, 2024 - Taylor & Francis
In the analysis of survey data, sampling weights are needed for consistent estimation of the
population; however, weights are typically modified through a process termed “calibration” to …
population; however, weights are typically modified through a process termed “calibration” to …