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A Fast Bootstrap Algorithm For Causal Inference With Large Data.

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A Fast Bootstrap Algorithm For Causal Inference With Large Data.

Related Experiment Video

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A fast bootstrap algorithm for causal inference with large data.

Matthew Kosko¹, Lin Wang², Michele Santacatterina³

¹Department of Statistics, George Washington University, Washington, DC.

Statistics in Medicine

|May 13, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new causal bag of little bootstraps method offers efficient causal effect estimation for large datasets. This computational improvement provides reliable confidence intervals, aiding causal inference in research and industry.

Keywords:

causal bootstrap covariate balance machine learning propensity score real‐world data

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Area of Science:

Statistics
Biostatistics
Epidemiology

Background:

Estimating causal effects from large datasets is crucial in research and industry.
The traditional bootstrap method for standard errors and confidence intervals is computationally intensive for large data.
Modern causal inference techniques further increase the computational burden of the bootstrap.

Purpose of the Study:

Introduce a novel bootstrap algorithm, the causal bag of little bootstraps (CB দাব)
Enhance computational efficiency for causal inference with large datasets.
Ensure consistent estimates and reliable confidence interval coverage.

Main Methods:

Developed the causal bag of little bootstraps (CB দাব) algorithm.
Evaluated algorithm performance using simulation studies.

Assessed bias, confidence interval coverage, and computational time.

Applied the method to a large observational dataset (Women's Health Initiative).

Main Results:

The CB দাব algorithm significantly improves computational efficiency compared to the traditional bootstrap.
The proposed method yields consistent estimates and desirable confidence interval coverage.
Simulation studies demonstrate the algorithm's effectiveness in bias and coverage.

Conclusions:

The causal bag of little bootstraps is a computationally efficient and statistically sound method for causal inference with large datasets.
This algorithm facilitates the evaluation of causal effects in complex, large-scale studies.
The method was successfully applied to analyze the effect of hormone therapy on coronary heart disease.