How can you use propensity score matching effectively?

1 Understand the assumptions

Propensity score matching relies on two main assumptions: strong ignorability and overlap. Strong ignorability means that the potential outcomes of the units are independent of their treatment assignment, conditional on their observed covariates. This implies that there are no hidden or unmeasured confounders that affect both the treatment and the outcome. Overlap means that there is a positive probability of receiving the treatment for every combination of covariates. This ensures that you can find a suitable match for each unit in the other group. You should check these assumptions before applying propensity score matching, and justify them with domain knowledge and data analysis.

2 Choose a matching method

There are different methods for matching units based on their propensity scores, such as nearest neighbor, caliper, stratification, and weighting. Each method has its own advantages and disadvantages, depending on the size and distribution of your data, the balance of your covariates, and the precision of your estimates. You should compare different methods and select the one that minimizes the bias and variance of your causal effect estimate, while preserving the sample size and representativeness of your data.

3 Assess the balance

After matching, you should assess the balance of your covariates across the treatment and control groups. This means that the distributions of the covariates should be similar in both groups, and that there should be no significant differences in their means or proportions. You can use graphical and numerical methods to check the balance, such as histograms, boxplots, standardized differences, and hypothesis tests. If the balance is not satisfactory, you may need to adjust your matching method, your propensity score model, or your covariate selection.

4 Estimate the causal effect

Once you have a matched sample, you can estimate the causal effect of the treatment on the outcome using different methods, such as regression, difference-in-differences, or inverse probability weighting. You should use appropriate methods to account for the matching design, the uncertainty of the propensity scores, and the potential heterogeneity of the effects. You should also report the confidence intervals and the sensitivity analysis of your estimates, to show how robust they are to potential violations of the assumptions.

5 Interpret the results

Finally, you should interpret the results of your propensity score matching analysis with caution and clarity. You should acknowledge the limitations of your data and your methods, and avoid making causal claims that are not supported by your evidence. You should also explain the meaning and implications of your estimates, and how they relate to the research question and the policy or practice context. You should communicate your results to your audience in a clear and accessible way, using visualizations, summaries, and examples.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?