Detailed estimator comparison¶
Estimator 
Treatment
Type

Requires
Instrument

Delivers Conf.
Intervals

Linear
Treatment

Linear
Heterogeneity

Mulitple
Outcomes

Multiple
Treatments

HighDimensional
Features



Any 
Yes 
Yes 
Assumed 
Yes 
Yes 

Any 
Yes 
Yes 
Yes 

Any 
Yes 
Yes 
Assumed 
Yes 
Yes 
Yes 

Categorical 
Yes 
Projected 
Yes 
Yes 

Any 
Yes 
Yes 
Assumed 
Yes 
Yes 

Categorical 
Yes 
Projected 
Yes 

Any 
Yes 
Yes 
Yes 
Yes 
Yes 

Categorical 
Yes 
Yes 
Yes 

Any 
Yes 
Yes 
Yes 
Yes 

Categorical 
Yes 
Yes 
Yes 

Categorical 
Yes 
Yes 
Yes 

Categorical 
Yes 
Yes 

Any 
Yes 
Assumed 
Yes 
Yes 
Yes 

1d/Binary 
Yes 
Yes 
Yes 
 Treatment Type
Some estimators can only estimate effects of particular kinds of treatments. Discrete treatments can be described by a finite number of comprehensive categories (for example, group A received a 10% discount on product 1, group B received a 10% discount on product 2, group C received no discounts). Binary treatments are a special case of discrete treatments with only two categories. Continuous treatments can take on any value along the number line (for example, minutes of exercise per week).
 Requires Instrument
Some estimators identify the causal effect of a treatment by considering only a subset of the variation in treatment intensity that is conditionally random given other data features. This subset of the variation is driven by an instrument, which is usually some kind of randomization (i.e. an earlier experiment or a lottery). See the Instrumental Variable Regression section for more information on picking a good instrument.
 Delivers Confidence Intervals
Many estimators can deliver analytic confidence intervals for the final treatment effects. These confidence intervals correctly adjust for the reuse of data across multiple stages of estimation. EconML cannot deliver analytic confidence intervals in cases where this multistage estimation is too complex or for estimators such as the MetaLearners that trade honest confidence intervals for model selection and regularization. In these cases it is still possible to get bootstrap confidence intervals, but this process is slow and may not be statistically valid.
 Linear Treatment
Some estimators impose the assumption that the outcome is a linear function of the treatment. These estimators can also estimate a nonlinear relationship between a treatment and the outcome if the structure of the relationship is known and additively separable (for example, the linear function could include both treatment and treatmentsquared for continuous treatments). These linear functions can also include specified interactions between treatments. However, these estimators cannot estimate a fully flexible nonparametric relationship between treatments and the outcome (for example, the relationship cannot be modeled by a forest).
 Linear Heterogeneity
The CATE function determines how the size of a user’s response to the treatment varies by user features. Some estimators impose the assumption that effect size is a linear function of user features. A few models estimate a more flexible relationship between effect size and user features and then project that flexible function onto a linear model. This second approach delivers a betterfitting linear approximation of a nonlinear relationship, but is less efficient in cases where you are confident assuming the true relationship is linear. Finally, some estimation models allow a fully flexible relationship between effect size and user features with no linearity structure.
 Multiple Outcomes
Some estimation models allow joint estimation of the effects of treatment(s) on multiple outcomes. Other models only accommodate a single outcome.
 Multiple Treatments
Some estimation models allow joint estimation of the effects of multiple treatments on outcome(s). Other models only accommodate a single treatment.
 HighDimensional Features
Many estimators only behave well with a small set of specified features, X, that affect the size of a user’s response to the treatment. If you do not already know which few features might reasonably affect the user’s response, use one of our sparse estimators that can handle large feature sets and penalize them to discover the features that are most correlated with treatment effect heterogeneity.