Sample Empirical Likelihood Methods for Causal Inference
arXiv · Methodology(2024)
Abstract
Causal inference is crucial for understanding the true impact of
interventions, policies, or actions, enabling informed decision-making and
providing insights into the underlying mechanisms that shape our world. In this
paper, we establish a framework for the estimation and inference of average
treatment effects using a two-sample empirical likelihood function. Two
different approaches to incorporating propensity scores are developed. The
first approach introduces propensity scores calibrated constraints in addition
to the standard model-calibration constraints; the second approach uses the
propensity scores to form weighted versions of the model-calibration
constraints. The resulting estimators from both approaches are doubly robust.
The limiting distributions of the two sample empirical likelihood ratio
statistics are derived, facilitating the construction of confidence intervals
and hypothesis tests for the average treatment effect. Bootstrap methods for
constructing sample empirical likelihood ratio confidence intervals are also
discussed for both approaches. Finite sample performances of the methods are
investigated through simulation studies.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话