Instead of knowing the average cuasal effect, we might want to know which units benefit and which units lose by being exposed to a treatment. For example, a cash transfer program might motivate some people to work harder and incentivize others to work less. Thus, we might want to know how the cash transfer program affects individuals instead of it average affect on the population. To do so, we can use metalearners. Depending on the scenario, we may want to use an S-learner, a T-learner, an X-learner, or an R-learner. The basic steps to use all three metalearners are below. The difference between the metalearners is how they estimate the CATE and what types of variables they can handle. In the case of S, T, and X learners, they can only handle binary treatments. On the other hand, R-learners can handle binary, categorical, count, or continuous treatments but only supports continuous outcomes.


For a deeper dive on S-learning, T-learning, and X-learning see:

Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for 
estimating heterogeneous treatment effects using machine learning." Proceedings of the 
national academy of sciences 116, no. 10 (2019): 4156-4165.

To learn more about R-learning see:

Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment 
effects." Biometrika 108, no. 2 (2021): 299-319.

Initialize a Metalearner

S-learners, T-learners, and X-learners all take at least three arguments: an array of covariates, a vector of outcomes, and a vector of treatment statuses.


Additional options can be specified for each type of metalearner using its keyword arguments.

# Generate data to use
X, Y, T =  rand(1000, 5), rand(1000), [rand()<0.4 for i in 1:1000]

# We could also use DataFrames
# using DataFrames
# X = DataFrame(x1=rand(1000), x2=rand(1000), x3=rand(1000), x4=rand(1000), x5=rand(1000))
# T, Y = DataFrame(t=[rand()<0.4 for i in 1:1000]), DataFrame(y=rand(1000))

s_learner = SLearner(X, Y, T)
t_learner = TLearner(X, Y, T)
x_learner = XLearner(X, Y, T)
r_learner = RLearner(X, Y, T)

Estimate the CATE

We can estimate the CATE for all the models by passing them to estimatecausaleffect!.


Get a Summary

We can get a summary of the models that includes p0values and standard errors for the average treatment effect by passing the models to the summarize method.

Calling the summarize methodd returns a dictionary with the estimator's task (regression or classification), the quantity of interest being estimated (CATE), whether the model uses an L2 penalty, the activation function used in the model's outcome predictors, whether the data is temporal, the validation metric used for cross validation to find the best number of neurons, the number of neurons used in the ELMs used by the estimator, the number of neurons used in the ELM used to learn a mapping from number of neurons to validation loss during cross validation, the causal effect, standard error, and p-value for the ATE.


Step 4: Validate the Model

We can validate the model by examining the plausibility that the main assumptions of causal inference, counterfactual consistency, exchangeability, and positivity, hold. It should be noted that consistency and exchangeability are not directly testable, so instead, these tests do not provide definitive evidence of a violation of these assumptions. To probe the counterfactual consistency assumption, we assume there were multiple levels of treatments and find them by binning the dependent vairable for treated observations using Jenks breaks. The optimal number of breaks between 2 and num_treatments is found using the elbow method. Using these hypothesized treatment assignemnts, this method compares the MSE of linear regressions using the observed and hypothesized treatments. If the counterfactual consistency assumption holds then the difference between the MSE with hypothesized treatments and the observed treatments should be positive because the hypothesized treatments should not provide useful information. If it is negative, that indicates there was more useful information provided by the hypothesized treatments than the observed treatments or that there is an unobserved confounder. Next, this methods tests the model's sensitivity to a violation of the exchangeability assumption by calculating the E-value, which is the minimum strength of association, on the risk ratio scale, that an unobserved confounder would need to have with the treatment and outcome variable to fully explain away the estimated effect. Thus, higher E-values imply the model is more robust to a violation of the exchangeability assumption. Finally, this method tests the positivity assumption by estimating propensity scores. Rows in the matrix are levels of covariates that have a zero probability of treatment. If the matrix is empty, none of the observations have an estimated zero probability of treatment, which implies the positivity assumption is satisfied.


One can also specify the maxium number of possible treatments to consider for the causal consistency assumption and the minimum and maximum probabilities of treatment for the positivity assumption with the num_treatments, min, and max keyword arguments.


Obtaining correct estimates is dependent on meeting the assumptions for interrupted time series estimation. If the assumptions are not met then any estimates may be biased and lead to incorrect conclusions.


For a thorough review of casual inference assumptions see:

Hernan, Miguel A., and James M. Robins. Causal inference what if. Boca Raton: Taylor and 
Francis, 2024.

For more information on the E-value test see:

VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: 
introducing the E-value." Annals of internal medicine 167, no. 4 (2017): 268-274.