Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation setups, and find promising performance relative to existing baselines.

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

We revisit the blazar sequence exploiting the complete, flux limited sample of blazars with known redshift detected by the Fermi satellite after 4 years of operations (the 3LAC sample). We divide the sources into gamma-ray luminosity bins, collect all the archival data for all blazars, and construct their spectral energy distribution (SED). We describe the average SED of blazars in the same luminosity bin through a very simple, completely phenomenological function consisting of two broken power laws connecting with a power law of fixed slope describing the radio emission. We do that separately for BL Lacs and for flat spectrum radio quasars (FSRQs) and also for all blazars together. The main results are: i) FSRQs display approximately the same SED as the luminosity increases, except for the fact that the relative importance of the high energy peak increases; ii) as a consequence, X-ray spectra of FSRQs become harder for larger luminosities; iii) BL Lacs form indeed a sequence: they become redder (i.e. the peak frequencies becomes smaller) for increasing luminosities, with a steeper gamma-ray slope and a larger dominance of the high energy peak; iv) for all blazars (BL Lacs+FSRQs) these properties becomes more prominent, as the highest luminosity bin is populated mostly by FSRQs and the lowest luminosity bin mostly by BL Lacs. This agrees with the original blazar sequence, although BL Lacs never have an average gamma-ray slope as hard as found in the original sequence. v) At high luminosities, a large fraction of FSRQs shows signs of thermal emission from the accretion disc, contributing in the optical-UV.

The Fermi blazar sequence

This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.

Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use estimated propensity scores to be more robust to confounding, and how they handle data with clustered errors.

Estimating Treatment Effects with Causal Forests: An Application

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the machine learning literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, as well as matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics, methods that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, problems that include causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.

Machine Learning Methods Economists Should Know About

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms-such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks-to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

/pdf/metalearners-for-estimating-heterogeneous-treatment-effects-52v0unvjm6.pdf

Metalearners for estimating heterogeneous treatment effects using machine learning

https://iopscience.iop.org/article/10.3847/0067-0049/224/2/26/pdf

A COMPREHENSIVE STATISTICAL DESCRIPTION OF RADIO-THROUGH-γ-RAY SPECTRAL ENERGY DISTRIBUTIONS OF ALL KNOWN BLAZARS

We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.

/pdf/transfer-learning-for-estimating-causal-effects-using-neural-j3qno5n64g.pdf

Transfer Learning for Estimating Causal Effects using Neural Networks

Estimating heterogeneous treatment effects has become increasingly important in many fields and life and death decisions are now based on these estimates: for example, selecting a personalized course of medical treatment. Recently, a variety of procedures relying on different assumptions have been suggested for estimating heterogeneous treatment effects. Unfortunately, there are no compelling approaches that allow identification of the procedure that has assumptions that hew closest to the process generating the data set under study and researchers often select one arbitrarily. This approach risks making inferences that rely on incorrect assumptions and gives the experimenter too much scope for $p$-hacking. A single estimator will also tend to overlook patterns other estimators could have picked up. We believe that the conclusion of many published papers might change had a different estimator been chosen and we suggest that practitioners should evaluate many estimators and assess their similarity when investigating heterogeneous treatment effects. We demonstrate this by applying 28 different estimation procedures to an emulated observational data set; this analysis shows that different estimation procedures may give starkly different estimates. We also provide an extensible \texttt{R} package which makes it straightforward for practitioners to follow our recommendations.

Causaltoolbox---Estimator Stability for Heterogeneous Treatment Effects

Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve their utility for locally smooth response surfaces, we study regression trees and random forests with linear aggregation functions. We introduce a new algorithm that finds the best axis-aligned split to fit linear aggregation functions on the corresponding nodes, and we offer a quasilinear time implementation. We demonstrate the algorithm's favorable performance on real-world benchmarks and in an extensive simulation study, and we demonstrate its improved interpretability using a large get-out-the-vote experiment. We provide an open-source software package that implements several tree-based estimators with linear aggregation functions.

Sören R. Künzel

Papers

Metalearners for estimating heterogeneous treatment effects using machine learning

A COMPREHENSIVE STATISTICAL DESCRIPTION OF RADIO-THROUGH-γ-RAY SPECTRAL ENERGY DISTRIBUTIONS OF ALL KNOWN BLAZARS

Transfer Learning for Estimating Causal Effects using Neural Networks

Causaltoolbox---Estimator Stability for Heterogeneous Treatment Effects

Linear Aggregation in Tree-based Estimators