# Machine Learning Based Estimation of Heterogeneous Treatment Effects¶

One of the biggest promises of machine learning is the automation of decision making in a multitude of application domains. A core problem that arises in most data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the effect of an intervention on an outcome of interest as a function of a set of observable characteristics of the treated sample? For instance, this problem arises in personalized pricing, where the goal is to estimate the effect of a price discount on the demand as a function of characteristics of the consumer. Similarly it arises in medical trials where the goal is to estimate the effect of a drug treatment on the clinical response of a patient as a function of patient characteristics. In many such settings we have an abundance of observational data, where the treatment was chosen via some unknown policy and the ability to run A/B tests is limited.

The EconML package implements recent techniques in the literature at the intersection of econometrics and machine learning that tackle the problem of heterogeneous treatment effect estimation via machine learning based approaches. These novel methods offer large flexibility in modeling the effect heterogeneity (via techniques such as random forests, boosting, lasso and neural nets), while at the same time leverage techniques from causal inference and econometrics to preserve the causal interpretation of the learned model and many times also offer statistical validity via the construction of valid confidence intervals.

It implements techniques from recent academic works, several of which produced in-house by the ALICE project of Microsoft Research, and many others from leading groups in the field. Examples include Double Machine Learning (see e.g. [Chernozhukov2016], [Chernozhukov2017], [Mackey2017], [Nie2017], [Chernozhukov2018], [Foster2019]), Causal Forests (see e.g. [Wager2018], [Athey2019] [Oprescu2019]), Deep Instrumental Variables (see e.g. [Hartford2017]), Non-parametric Instrumental Variables [Newey2003], meta-learners (see e.g. [Kunzel2017]). The library brings together all these diverse techniques under a common python API.

# Motivating Examples¶

## Customer Targeting¶

An important problem in modern business analytics is building automated tools to prioritize customer acquisition and personalize customer interactions to increase sales and revenue. Typically businesses will offer personalize incentives to customers to increase spend or increase the level of engagement via more human resources. Any such personalized intervention corresponds to a monetary investment and the main question that business analytics are called to answer is: what is the return on investment (ROI)?

Analyzing the ROI is inherently a treatment effect question: what was the effect of any investment on a particular customer on its spend? Understanding how these return on investment varies across customers can enable more targeted investment policies and increased ROI via better targeting. Using historical data from deployed investments, and estimating the heterogeneous treatment effect via any of the proposed methods, business analysts can learn in an automated manner, data-driven customer targeting and prioritization policies.

## Personalized Pricing¶

Personalized discounts have become very widespread in the digital economy. To set the optimal personalized discount policy a business needs to understand what is the effect of a drop in price on the demand of a customer for a product as a function of customer characteristics. The estimation of such personalized demand elasticities can also be phrased in the language of heterogeneous treatment effects, where the treatment is the price (or typically log of price) on the demand (or typically log of demand) as a function of observable features of the customer. Hence, estimation of heterogeneous treatment effects can lead to optimal pricing policies.

## Stratification in Clinical Trials¶

Which patients should be selected for a clinical trial? If we want to demonstrate that a clinical treatment has an effect on at least some subset of a population, then fully randomized clinical trials are inappropriate as they will solely estimate average effects. Using heterogeneous treatment effect techniques, we can use observational data to come up with estimates of these effects and identify good candidate patients for a clinical trial that our model estimates have high treatment effects.

## Learning Click-Through-Rates¶

In the design of a page layout and more importantly in ad placement, it is important to understand the click-through-rate of page components (e.g. ads) on different positions of a page. Even though the modern approach is to run multiple A/B tests, when such page components involve revenue considerations (such as ad placement), then observational data can help guide correct A/B tests to run. Heterogeneous treatment effect estimation can provide estimates of the click-through-rate of page components from observational data. In this setting, the treatment is simply whether the component is placed on that page position and the response is whether the user clicked on it.