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How competitive is propensity score matching using address embeddings with supervised classification for record linkage tasks? Sam Comber1, Dani ArribasBel1 and Ronald Nyakairu2 1 University of Liverpool,
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How to fill out use of propensity score

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Step 1: Define the research question or hypothesis
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Step 2: Identify the treatment or intervention being studied
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Step 3: Gather data from the study population
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Step 4: Identify potential confounding variables
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Step 5: Calculate propensity scores for each individual
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Step 6: Assess balance of covariates between treatment groups
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Step 7: Analyze treatment effect using propensity score
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Step 8: Interpret the results and draw conclusions

Who needs use of propensity score?

01
Researchers conducting observational studies
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Healthcare professionals evaluating treatment effectiveness
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Policy makers assessing the impact of interventions
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Epidemiologists studying population health outcomes
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Social scientists analyzing program interventions
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The use of propensity score is primarily to control for confounding variables in observational studies by estimating the probability of a subject receiving a particular treatment based on observed characteristics.
Researchers and statisticians conducting observational studies that require adjustment for confounding factors are typically required to use and report propensity scores.
Filling out the use of propensity score involves calculating the propensity scores using logistic regression or other statistical methods, performing matching or weighting based on these scores, and reporting the methodology and results in the study.
The purpose of using a propensity score is to create a balanced comparison between treatment groups in observational studies, thereby reducing bias and allowing for more accurate estimation of treatment effects.
Reports on the use of propensity scores should include the method of calculation, the covariates used, the balance checks performed, any matching or weighting techniques applied, and the overall impact on study results.
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