The balancing act in causal inference

E Ben-Michael, A Feller, DA Hirshberg… - arXiv preprint arXiv …, 2021 - arxiv.org
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 …

Sensitivity analysis for survey weights

E Hartman, M Huang - Political Analysis, 2024 - cambridge.org
Survey weighting allows researchers to account for bias in survey samples, due to unit
nonresponse or convenience sampling, using measured demographic covariates …

Towards representation learning for weighting problems in design-based causal inference

O Clivio, A Feller, C Holmes - arXiv preprint arXiv:2409.16407, 2024 - arxiv.org
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 …

Augmented balancing weights as linear regression

D Bruns-Smith, O Dukes, A Feller… - arXiv preprint arXiv …, 2023 - arxiv.org
We provide a novel characterization of augmented balancing weights, also known as
automatic debiased machine learning (AutoDML). These popular doubly robust or double …

Soft calibration for selection bias problems under mixed-effects models

C Gao, S Yang, JK Kim - Biometrika, 2023 - academic.oup.com
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 …

Hierarchically regularized entropy balancing

Y Xu, E Yang - Political Analysis, 2023 - cambridge.org
We introduce hierarchically regularized entropy balancing as an extension to entropy
balancing, a reweighting method that adjusts weights for control group units to achieve …

[PDF][PDF] Augmented balancing weights as undersmoothed regressions

D Bruns-Smith, O Dukes, A Feller, EL Ogburn - Cited on, 2023 - eogburn.com
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 …

kpop: a kernel balancing approach for reducing specification assumptions in survey weighting

E Hartman, C Hazlett, C Sterbenz - Journal of the Royal …, 2024 - academic.oup.com
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 …

Approximate balancing weights for clustered observational study designs

E Ben‐Michael, L Page, L Keele - Statistics in Medicine, 2024 - Wiley Online Library
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 …

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 …