*A comprehensive collection of state-of-the-art methods from causal machine learning or uplift modeling to estimate individualized treatment effects.*

- Motivation
- Notation
- Indirect Estimation
- Direct Estimation
- Benchmark Studies

Assume that we want to change the world. A little less grandiose, assume we want to take an action that will impact an outcome to be more like we prefer. A little more applied, fields like medicine and marketing make a decision to take action that increases life expectation by five years or makes grandma spend another dollar in the supermarket. To make that decision, we want to know in advance what effect the conceivable treatment will have on each individual/grandma. Remember treatment as a general term that can mean anything from a mail catalog to earlier starting time for a class to medication.

Example questions and treatments are:

- How much more likely is a my middle-aged, sporty patient to be cured when given new medication as opposed to the standard medication?
- How much more likely are participants to complete the online training if we send them a progress report every week? Or are there certain participants that react negatively to perceived pressure?
- How much more will a customer buy if promised free shipping as opposed to not showing any banner?

Estimation of treatment effects on the individual level is a tricky problem, because we typically only have one shot at applying a treatment: either my grandma gets the coupon or she doesn’t and we will be left to ponder the ‘what if’. ‘What if’ type of questions belong to the domain of causal inference and estimating the treatment effect for individuals has become a task at the intersection of causal inference and machine learning. The same problem is known as heterogeneous treatment effects in social studies and medicine, conditional average treatment effects in econometrics and uplift modeling or prescriptive analytics in business intelligence.

The fundamental problem of ‘what if’ is that we can only apply one treatment to each individual and observe their outcome. Since we don’t know how the individual would have reacted under alternative treatment, we can never calculate the real treatment effect. What we can do is to look at a group of individuals who have received different treatments and the difference in their outcomes.

Research into estimating treatment effects from experiments and observational studies goes far into the last century, but the last ten years have seen renewed interest to leverage machine learning techniques to increase the power of known methods or build machine learning models specialized in estimating treatment effects.

Unfortunately, research in different fields is fractured, with different termini and notation. The following list is meant as an incomplete, but comprehensive summary of state-of-the-art methods from bioinformatics, computer science, econometrics and business studies to estimate individualized treatment effects. I will assume that we know how randomized experiments work and that we have data on some individuals who have gotten treatment and some who haven’t.

My notation roughly follows the econometric literature:

*What we know about the person:*

Covariates for individual *i*: \( X_i \)

*Their chance to get the coupon from us:*

(Estimated) Propensity score: \( P(W=w|X), e(X)=P(T=1|X) \text{ when } T \in {0;1} \)

*If the person got a coupon or not:*

Treatment Group Indicator: \(T\)

*If/How much they bought:*

(Potential) Outcome *Y* for individual *i* under group assignment *T*: \( Y_i(T)\)

*If/How much we think they should have bought:*

(Estimated) Conditional outcome under group assignment *T*: \( \mu(X_i, T_i) \)

*How much impact (we think) the coupon had:*

(Estimated) Treatment Effect: \( \tau_i \)

Approaches to treatment effect estimation can be classified into indirect and direct approaches, with some approaches in between. Indirect approaches estimate the treatment effect using one or more models of the observed outcome. Estimating the outcome is an indirect route to the treatment effect, since we are typically not interested in a prediction of the outcome and the process that the outcome model captures is not the process of how the treatment effect changes. Since we do not observe the actual treatment effect, modeling the outcome allows us to transport our knowledge from predictive modeling to the causal domain.

**(aka K-Model approach, Two-Model Approach, T-Learner, Conditional Mean Regressions)**

The following approaches predict the outcome with and without treatment. By looking at the *difference* between the predicted outcomes or *expected mean conditional on some covariates*, we can find out how much impact we can expect from the treatment.

When using the k-model approach, named after the *k* number of models we need to train, we estimate an outcome model for each treatment group separately and calculate the treatment effect as the difference between the estimated outcomes.

If there is one treatment, we will have two groups: treatment and control (where people might get nothing or Placebo). The impact of treatment vs. doing nothing is then the difference between the prediction of the model build on the treated and prediction of the model build on the control group.

The outcome models (*base learners*) can take any form, so we could use neural networks or boosted trees to do the heavy lifting. The downside of the k-model approach is that the outcome process may be difficult to estimate and that the errors of the two models in the difference may add up.

**(aka DragonNet)**

We can of course use two neural networks as outcome models to estimate the conditional means. The two outcome models are likely very similar, since both approximate to a large extent the outcome process without treatment. We may be able to gain efficiency and improve calibration through parameter sharing in the lower hidden layers. The architecture is then best understood as a single multi-task network, with one loss calculated on the control group observations and one (or more) loss calculated on the treatment group observations.

The multi-task architecture has an additional advantage when working with observational data. When we cannot conduct an experiment and treatment assignment is not random, we can correct for variables that impact the treatment assignment to still make unbiased estimates. It is in fact sufficient to correct only for the variables that impact treatment assignment (*propensity weighting*). An efficient way to filter the relevant information in the multi-task neural network is to correct the shared hidden layers. We correct the last shared layer, for example, by adding the treatment probability as an additional output. Predicting the treatment probability forces the hidden layers to distill the information that is necessary to predict treatment assignment and focus less on the information that is relevant only for outcome prediction, but doesn’t differ between the control and treatment group.

*Shalit, U., Johansson, F. D., & Sontag, D. (2017). Estimating individual treatment effect: generalization bounds and algorithms. Proceedings of the 34th International Conference on Machine Learning (ICML 2017).
Shi, C., Blei, D. M., & Veitch, V. (2019). Adapting Neural Networks for the Estimation of Treatment Effects. ArXiv:1906.02120 [Cs, Stat].
Alaa, A. M., Weisz, M., & van der Schaar, M. (2017). Deep Counterfactual Networks with Propensity-Dropout. ArXiv E-Prints, arXiv:1706.05966.*

**(aka G-computation)**

In settings where the treatment and control group vary in size, we may want to emphasize the conditional mean model estimated on the larger group.

Construct a treatment estimate for the treatment and control group separately using the conditional mean model from the other group: \[ T_i^1 = Y_i(1) - E[Y(0)|X=x] T_i^0 = E[Y(1)|X=x] - Y_i(0) \]

Project the treatment estimates on variables *X* directly within each group. Combine the treatment effect estimates from both projection models using a weighted average with weights manually chosen or equal to the estimated propensity score.
\[
\hat{\tau} = w(x)\hat{\tau_0} + (1-w(x))\hat{\tau_1}
\]

The name refers to the *cross* use of the conditonal mean of one group in the construction of the treatment estimate for the other group.

[TODO] The conditonal mean correction and propensity weighting make the X-Learner look like a variation on double robust estimation with treatment effect projection to me.

*Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165.
Robins J. A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Math Model. 1986;7(9–12):1393–1512.*

**(aka Single-Model-Approach, S-Learner)**

We can include the treatment indicator as a covariate into the model, optionally with interaction to other covariates. This is the way that typical regression analysis is used to estimate the average treatment effect on the population. In typical regression analysis, the treatment effect is captured by a single coefficient on the treatment indicator varible. When there are variable interactions or we use a more flexible or non-parametric model, we can predict the ITE via the difference of predicting the outcome for an observation with treatment set to 0 and set to 1. Training a single model for the outcome is simple and often interpretable.

A regression model with a linear additive treatment effect and interaction effects would look like this: \[ Y = \beta_0 + \tau_0 D_i+ \tau D_i X_i + \beta X_i + \epsilon_i \]

Under linear regression, the interaction effects between all variables and the treatment indicator blow up the dimensionality quickly. Instead, we could use any machine learning model and include the treatment indicator as a variable. However, if the treatment effect is small relative to other effects on the outcome, then regularized machine learning methods may ignore the treatment variable completely.

The advantage of response model that include the treatment variable directly is that they are flexible when modeling multiple treatments or continuous treatments. We always train a single model. To predict the ITE, we feed in \(X\) multiple times, each time with the treatment variable set to the value of interest. In the binary case, we would set the treatment variable once to 1 and once to 0 for the same \(X\). The difference in predictions is the treatment effect estimate.

*Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165.*

The single model approach works with any outcome model, but using Bayesian Additive Regression Trees (BART) has been shown to work well in several applied papers. BART is an analogue to gradient boosted decision trees estimated using Bayesian inference via Markov Chain Monte Carlo. The difference in posterior distributions gives a good estimate of the ITE in practice. The Bayesian treatment makes it easy to also give an uncertainty estimate of the treatment effect that may be useful for risk-conscious decision-making or to do uncertainty-based data collection.

*Hill, J. L. (2011). Bayesian Nonparametric Modeling for Causal Inference. Journal of Computational and Graphical Statistics, 20(1), 217–240.*

Direct approaches estimate the treatment effect without modeling the process that generates the observed outcomes. They instead capture the process the generates the individual-level treatment effect. Our intuition is that the outcome process is typically more complex than the treatment effect process. For example, a customer’s buying process is a complicated decision depending on awareness, context, money and shopping intention. A customer’s susceptibility to a coupon also depends on money and shopping intention, but is intuitively less personal and much less complicated.

Direct estimation approaches focus on treatment effect process. Many direct approaches nevertheless require an outcome model as an intermediate step to make estimation of the unobservable treatment effect feasible.

We can improve model calibration in the two-model framework by 1) constructing estimates in the treatment group as an addition of the control and treatment model and 2) joint model training. The first approach is a combination of an outcome model with a treatment effect model with the following intuition: An outcome model will estimate the effect of the covariates on the outcome ignoring the treatment variable. Its prediction will model the observed outcome with some residual not covered by the information available in the covariates. For the control group, we believe that the residual is random noise. For the treatment group, the residual is the same noise plus the effect of the treatment. We then train a second model on residuals of the outcome model for the treatment group, which captures the remaining information due to the treatment effect. The second model predicts the treatment effect directly.

*Hahn, P. R., Murray, J. S., & Carvalho, C. M. (2017). Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects. SSRN Electronic Journal.*

Neural network can be combined so that one network predicts the treatment effect directly. Look at the observed outcomes under treatment as a sum of the outcome without treatment and the treatment effect. Then we could estimate one network that predicts the outcome without treatment for all observations and a second network that predicts the treatment effect that needs to be added for treated individuals, equivalent to the residual left by the outcome network for treated observations. Instead of two outcome models, this framework leaves us with one network that predicts the outcome and one network that predicts the treatment effect. \[ Y = \text{nnet}_0 + T_i * \text{nnet}_1 \] To ensure that the networks are in tune with each other, we should train them jointly. This does not require much effort: For each observation, we sum up the prediction of the outcome network and the prediction of the treatment network multiplied by the treatment indicator. We then backpropagate the error through both networks.

*Farrell, M. H., Liang, T., & Misra, S. (2018). Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. ArXiv E-Prints, arXiv:1809.09953. (see Section 5.1. Implementation Details)*

Add a single model after any approach to estimate the ITE and predict the estimated ITE directly from the covariates. Treatment effect projection is useful to reduce complexity, increase prediction speed, faciliate interpretation or add additional regularization. When multiple models are used to estimate the ITE, for example as part of the k-model approach, a single model can replace the *k* models and reduce the time necessary to make a prediction. The treatment effect projection model could be linear regression which is easier to interpret than approaches containing multiple models.

*Foster, J. C., Taylor, J. M. G., & Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine, 30(24), 2867–2880.*

**(aka Modified Outcome Method, Class Variable Transformation, Generalized Weighted Uplift Method)**

Our job would be so much easier if we knew the actual treatment effect and could just train a regression model to predict it, but we can never know the actual treatment effect for an individual. However, we can find an artificial variable that is equal to the treatment effect in expectation.

The proxy variable is a transformation of the observed outcome for the individual: \[ Y^{TO}_i = T_i Y_i - (1-T_i) Y_i \]

Or including treatment propensity correction if we didn’t do a 50:50 randomized experiment: \[ Y^{IPW}_i = T_i \cdot \frac{Y_i}{e(X_i)} - (1-T_i) \cdot \frac{Y_i}{1-e(X_i)} \]

The transformed outcome is a noisy but unbiased estimate of the treatment effect. As an unbiased estimate, it can be used as a target variable for model training. The transformed outcome can also be used to calculate a feasible estimate of the MSE between model estimate and true treatment effect that is useful for model comparison.

*Hitsch, G. J., & Misra, S. (2018). Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation. SSRN.*

The transformed outcome including treatment propensity correction and conditional mean centering is \[ Y^{DR}_i = E[Y|X_i, T_i=1] - E[Y|X_i, T_i=0] + \frac{T_i(Y_i-E[Y|X_i, T_i=1])}{e(X_i)} - \frac{(1-T_i)(Y_i-E[Y|X_i, T_i=0])}{1-e(X_i)} \] Double robust esimation has two steps. In the first, we use effective models of our choice to estimate \(E[Y|X_i, T_i=1]\), \(E[Y|X_i, T_i=0]\) and \(e(X_i)\). In the second, we calculate \(Y^{DR}_i\) and train a model on transformed outcome variable.

*Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. Statistical Science, 22(4), 523–539.
Knaus, M. C., Lechner, M., & Strittmatter, A. (2019). Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. IZA Discussion Paper, 12039.*

**(a.k.a. Pollienated Outcome Tree)**

Transformed-outcome trees are tree build on the transformed outcome variable with the common CART algorithm. However, they do not return unbiased treatment estimates, because the ratio of treatment to control group observations varies for each leaf.

A better approach is to build a tree on the transformed outcome, but replace the average of the transformed outcome in each leaf \( \bar{Y}^{TO} \) with a better estimate of the treatment effect in that leaf. Given the group of observations in the leaf, we can estimate the average treatment effect in the leaf by the difference in the averages between treatment and control group, corrected for the probability of the observations to fall in their group \( e_i \) for cases other than 50:50 randomized experiments

\[\hat{\tau}^{\text{ATE}}_{\text{leaf}} =\frac{\sum_{i \in \text{leaf}} Y_i \cdot W_i / e_i }{\sum_{i \in \text{leaf}} W_i/e_i } -\frac{\sum_{i \in \text{leaf}} Y_i \cdot (1-W_i) / (1-e_i) }{\sum_{i \in \text{leaf}} (1-W_i)/(1-e_i) }\]The ATE estimate will be biased, because we use the same data twice. Once to build the tree structure to maximize variance between the leaves, and once to estimate the ATE in the leaves. Causal trees avoid bias by building the structure of the tree on one random half of the training data and calculating the leaf estimates on the other half (*honest splitting*).

If ITE estimates are unbiased, it turns out that we do not need to use the transformed outcome as splitting criterion. Only if the ITE estimates are unbiased, it is sufficient to optimize the decision tree splits by maximizing the variance between treatment effects in the leaves. The splitting criterion for causal trees then simplifies to the MSE/variance splitting criterion used in CART regression

\[ S = n_{\text{leaf}} \; \hat{\tau}_{\text{leaf}}^2 \]

*Athey, S., & Imbens, G. W. (2015). Machine Learning Methods for Estimating Heterogeneous Causal Eﬀects.
(Rzepakowsk, P., & Jaroszewics, S. (2010). Decision Trees for Uplift Modeling. 2010 IEEE International Conference on Data Mining.)
Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.*

Causal trees can be bagged or boosted like other models. Confidence intervals and consistency proofs exist for causal forests.

*Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839
Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous treatment effect estimation in high-dimensions. Statistics in Medicine, 37(11).*

*Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148–1178.*

Multivariate Adaptive Regression Splines (MARS) are related to the trees discussed above. They are reported to perform well by Powers et al. (2018), but perform less well in the simulations conducted by Wendling et al. (2018).

*Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous treatment effect estimation in high-dimensions. Statistics in Medicine, 37(11).*

**(aka Modified Covariate Method)**

Optimize a model \( \tau(X_i)\) for a loss function

\[ \underset{\tau}{\arg\min} \frac{1}{N}\sum_i (2T_i-1) \frac{T_i - e(X_i)}{4 e(X_i)(1-e(X_i))} (2(2T_i-1) Y_i - \tau(X_i))^2 \]

*(Tian, L., Alizadeh, A. A., Gentles, A. J., & Tibshirani, R. (2014). A simple method for estimating interactions between a treatment and a large number of covariates. Journal of the American Statistical Association, 109(508), 1517–1532.)
Knaus, M. C., Lechner, M., & Strittmatter, A. (2019). Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. IZA Discussion Paper, 12039.*

Optimize a model \( \tau(X_i)\) for a loss function based on a decomposition of the outcome function: \[ \underset{\tau}{\arg\min} \frac{1}{n}\sum_i \left( (Y_i − E[Y|X_i])− (T_i − E[W=1|X_i]) \tau(X_i) \right) \] The nuisance function for the conditional outcome and the proponsity score are estimated separately and an second-stage model trained on the transformation loss.

The name is a hommage to Peter M. Robinson and the residualization in the decomposition.

*Nie, X., & Wager, S. (2017). Quasi-Oracle Estimation of Heterogeneous Treatment Effects. ArXiv:1712.04912.*

These studies compare at least a subset of the methods in a structured setting. The literature seems divided into studies on health, medical and social studies concernced with performance on few observations, especially with many covariates, and studies on marketing with large scale A/B tests. I therefore recommend reading the simulation settings and data descriptions carefully and match them to the target application.

- Devriendt, F., Moldovan, D., & Verbeke, W. (2018). A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: a stepping stone toward the development of prescriptive analytics. Big Data, 6(1), 13–41. https://doi.org/10.1089/big.2017.0104
- Gubela, R. M., Bequé, A., Gebert, F., & Lessmann, S. (2019). Conversion uplift in e-commerce: A systematic benchmark of modeling strategies. International Journal of Information Technology & Decision Making, 18(3), 747–791.
- Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165.
- Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2017). Some methods for heterogeneous treatment effect estimation in high-dimensions. CoRR, arXiv:1707.00102v1.
- Wendling, T., Jung, K., Callahan, A., Schuler, A., Shah, N. H., & Gallego, B. (2018). Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases. Statistics in Medicine, 37, 3309–3324. https://doi.org/10.1002/sim.7820

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