Glossary

Base model

A prediction model used within a MetaLearner. See Kuenzel et al. (2019).

Conditional Average Treatment Effect (CATE)

\(\tau(X) = \mathbb{E}[Y(1) - Y(0)|X]\) in the binary case and \(\tau_{i,j}(X) = \mathbb{E}[Y(i) - Y(j)|X]\) if more than two variants exist. See Athey et al. (2016), Chapter 10.

Conditional Average Outcomes

\(\mathbb{E}[Y_i(w) | X]\) for each treatment variant \(w\).

Covariates

The features \(X\) based on which a CATE is estimated.

Double Machine Learning

Similar to the R-Learner, the Double Machine Learning blueprint relies on estimating two nuisance models in its first stage: a propensity model as well as an outcome model. Unlike the R-Learner, the last-stage or treatment effect model might need to be a specific type of estimator. See Chernozhukov et al. (2016).

Heterogeneous Treatment Effect (HTE)

Synonym for CATE.

MetaLearner

CATE model which relies on arbitrary prediction estimators (regressors or classifiers) for the actual estimation. See Kuenzel et al. (2019).

Nuisance model

A first-stage model in a MetaLearner. See Nie et al. (2019).

Observational data

Experiment data collected outside of a RCT, i.e. treatment assignments can depend on covariates or potential outcomes. See Athey et al. (2016).

Outcome model

A model estimating the outcome based on covariates, i.e. \(\mathbb{E}[Y|X]\).

Potential outcomes

Outcomes under various variants, e.g. \(Y(0)\) and \(Y(1)\), in Rubin-Causal Model (RCM). See Holland et al. (1986).

Propensity model

A model estimating the propensity score.

Propensity score

The probability of receiving a certain treatment/variant, conditioning on covariates: \(\Pr[W_i = w | X]\). See Rosenbaum et al. (1983).

Randomized Control Trial (RCT)

An experiment in which the treatment assignment is independent of the covariates \(X\). See Athey et al. (2016).

Treatment effect model

A second-stage model in a MetaLearner which models the treatment effects as a function of covariates.