Propensity Score Matching. When in doubt use this general purpose algorithm. Its implementation using the CausalInference library tends to be fast and practical for most datasets. Average treatment effects are estimated using nearest-neighbor propensity score matching. Propensity estimation is done via a logistic regression. In cases where the CausalInference implementation fails, the DoWhy implementation is generally more robust but also significantly slower.

Propensity Score Blocking. In cases where the dataset is imbalanced or not well randomized, traditional propensity score matching may not be accurate. The blocking approach, where blocks of records are grouped together for estimation rather than matching individual records, may yield better treatment estimates.

Propensity Score Stratification. In cases where the dataset is imbalanced or not well randomized, traditional propensity score matching may not be accurate. The stratification approach, where records are grouped together in stratas for estimation rather than matching individual records, may yield better treatment estimates. This implementation by the DoWhy library is very similar to Propensity Score Blocking in the CausalInference library.

Propensity Score Weighting. In cases where the dataset is small, traditional propensity score matching may drop too many records during the marching process. The weighting approach will keep all valid records by weighting them rather than dropping records, and may yield better treatment estimates when sample size is a concern.

Ordinary Least Squares. The OLS method attempts to fit a linear model to the data to adjust for other variables when inferring treatment effects. It is one of the simplest methods and can be fast but also inaccurate if the problem being modeled is not inherently linear. For a better methodology, use the traditional Propensity Score Matching approach. If your dataset is imbalanced or not randomized you may want to consider Propensity Score Blocking or Propensity Score Stratification.