=============================
Detailed estimator comparison
=============================
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| Estimator | | Treatment | | Requires | | Delivers Conf. | | Linear | | Linear | | Mulitple | | Multiple | | High-Dimensional |
| | | Type | | Instrument | | Intervals | | Treatment | | Heterogeneity | | Outcomes | | Treatments | | Features |
+=============================================+==============+==============+==================+=============+=================+============+==============+====================+
| :class:`.NonparametricTwoStageLeastSquares` | Any | Yes | | Yes | Assumed | Yes | Yes | |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.DeepIV` | Any | Yes | | | | Yes | Yes | |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.SparseLinearDML` | Any | | Yes | Yes | Assumed | Yes | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.SparseLinearDRLearner` | Categorical | | Yes | | Projected | | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.LinearDML` | Any | | Yes | Yes | Assumed | Yes | Yes | |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.LinearDRLearner` | Categorical | | Yes | | Projected | | Yes | |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.CausalForestDML` | Any | | Yes | Yes | | Yes | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.ForestDRLearner` | Categorical | | Yes | | | | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.DMLOrthoForest` | Any | | Yes | Yes | | | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.DROrthoForest` | Categorical | | Yes | | | | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :ref:`metalearners ` | Categorical | | | | | Yes | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.DRLearner` | Categorical | | | | | | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.DML` | Any | | | Yes | Assumed | Yes | Yes | Yes |
+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
| :class:`.NonParamDML` | 1-d/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 multi-stage 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 non-linear 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 treatment-squared for continuous treatments). These linear functions can also
include specified interactions between treatments. However, these estimators cannot estimate a fully
flexible non-parametric 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 better-fitting linear approximation of a
non-linear 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.
High-Dimensional 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.