In random forests, the data is repeatedly split in order to minimize prediction error of an outcome variable. Causal forests are built similarly, except that instead of minimizing prediction error, data is split in order to maximize the difference across splits in the relationship between an outcome variable and a “treatment” variable.
Full Answer
Can causal forests be used to study treatment heterogeneity?
We applied causal forests to study treatment heterogeneity on a dataset derived from the National Study of Learning Mindsets. Two challenges in this setting involved an observational study design with unknown treatment propensities, and clustering of outcomes at the school level.
What is causal forest method used for?
More broadly, the causal forest method has been used to evaluate the impact of weather on agricultural productivity [43] as well as effectiveness of forest management policies [44], fishery policies [45], and growth mindset interventions [46]. ...
How do causal forests deal with overfitting?
To deal with overfitting, causal forests use an honesty condition, whereby a tree is honest, if for each training sample ( i) it only uses the response (Y ᵢ) to estimate the within-leaf treatment effect or to decide where to place the split, but not both ( Jacob, 2021 ).
How can I estimate the number of causal forests?
The grf package has a causal_forest function that can be used to estimate causal forests. Additional functions afterwards can estimate, for example, the average_treatment_effect (). See help (package='grf') for more options.
How does causal forest work?
While a random forest is built from decision trees , a causal forest is built from causal trees, where the causal trees learn a low-dimensional representation of treatment effect heterogeneity . Importantly, the splitting criterion optimizes for finding splits associated with treatment effect heterogeneity.
Can random forests learn causality?
Causal forests are a causal inference learning method that are an extension of Random Forests. In random forests, the data is repeatedly split in order to minimize prediction error of an outcome variable.
What is causal tree?
In summary, the causal factor tree is an investigation/analysis tool that is used to display a logical hierarchy of all the causes leading to a given effect or consequence. When gaps in knowledge are encountered, the tree exposes the gap, but does not provide any means to resolve it; other tools are required.
What is conditional average treatment effect?
Heterogenous treatment effects If the average treatment effects are different, SUTVA is violated. A per-subgroup ATE is called a "conditional average treatment effect" (CATE), i.e. the ATE conditioned on membership in the subgroup. CATE can be used as an estimate if SUTVA does not hold.
What is causal forecasting?
Causal forecasting is a strategy that involves the attempt to predict or forecast future events in the marketplace, based on the range of variables that are likely to influence the future movement within that market.
What is Debiased machine learning?
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals (i.e. scalar summaries) of machine learning algorithms. For example, an analyst may desire the confidence interval for a treatment effect estimated with a neural network.
What are causal factors?
A causal factor is a collective descriptive term associated with human performance or a safety management system which can be broken down to identify direct, root, and contributing causes.
What is heterogeneous treatment effects?
Heterogeneity of treatment effect (HTE) is the nonrandom, explainable variability in the direction and magnitude of treatment effects for individuals within a population.
How does fault tree analysis work?
Fault tree analysis (FTA) is a graphical tool to explore the causes of system level failures. It uses boolean logic to combine a series of lower level events and it is basically a top-down approach to identify the component level failures (basic event) that cause the system level failure (top event) to occur.
What is a differential treatment effect?
Heterogeneity of treatment effect (HTE), better called differential treatment effect, is variation in a measure of treatment effect on a scale for which it is mathematically possible that such variation be absent even if the treatment has a nonzero effect.
What is treatment effect in statistics?
Treatment effects can be estimated using social experiments, regression models, matching estimators, and instrumental variables. A 'treatment effect' is the average causal effect of a binary (0–1) variable on an outcome variable of scientific or policy interest.
How do you evaluate the treatment effect?
When a trial uses a continuous measure, such as blood pressure, the treatment effect is often calculated by measuring the difference in mean improvement in blood pressure between groups. In these cases (if the data are normally distributed), a t-test is commonly used.
Abstract
To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables.
JEL Classification
C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Authors
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1 Methodology and Motivation
There has been considerable recent interest in methods for heterogeneous treatment effect estimation in observational studies (Athey and Imbens, 2016; Athey, Tibshirani, and Wager, 2019; Ding, Feller, and Miratrix, 2016; Dorie, Hill, Shalit, Scott, and Cervone, 2017; Hahn, Murray, and Carvalho, 2017; Hill, 2011; Imai and Ratkovic, 2013; Künzel, Sekhon, Bickel, and Yu, 2017; Luedtke and van der Laan, 2016; Nie and Wager, 2017; Shalit, Johansson, and Sontag, 2017; Su, Tsai, Wang, Nickerson, and Li, 2009; Wager and Athey, 2018; Zhao, Small, and Ertefaie, 2017) .
2 Workshop Results
We now use our causal forest as trained in Algorithm 1 to explore the questions from Section 1.1.
3 Post-workshop analysis
Two notable differences between the causal forest analysis used here and a more direct machine-learning -based analysis were our use of cluster-robust methods, and of orthogonalization for robustness to confounding as in (
4 Discussion
We applied causal forests to study treatment heterogeneity on a dataset derived from the National Study of Learning Mindsets. Two challenges in this setting involved an observational study design with unknown treatment propensities, and clustering of outcomes at the school level.
lbuckley13 commented on May 15, 2018
I have the same question as you. The best answer that I've found was published by a group at Mt. Sinai Medical School using a neutral randomized controlled trial of a weight loss intervention in patients with type 2 diabetes mellitus.
swager commented on May 18, 2018
Also, as to the original question of looking for variables that are associated with treatment heterogeneity, I often try something like this:
jtibshirani commented on May 30, 2018
Concerning the OOB predictions, here's what I propose: by default, we should calculate OOB automatically during training, and return them as part of the forest object ( forest$oob_predictions = predict_oob (...) ).
Zaw5009 commented on Jun 25, 2018
Hi, I am looking to calculate ATEs (and their SEs) for predicted deciles based on predictions from a causal forest. A version of this is done in the Uplift package, but in that package 1) causal forests aren't used and 2) you have to bootstrap SEs.
Zaw5009 commented on Jul 5, 2018
I have not tried the bootstrapping approach, but this solves my problem. I can just plug in the values from the holdout group to obtain lift deciles and then calculate the ATEs and their accompanying SEs for each predicted lift decile. Thanks!
swager commented on Jul 6, 2018
Thanks a lot @markhwhiteii! We'll make sure this functionality is included in the next release.
tianshengwang commented on Jan 31, 2020
Also, as to the original question of looking for variables that are associated with treatment heterogeneity, I often try something like this:
Description
Trains a causal forest that can be used to estimate conditional average treatment effects tau (X). When the treatment assignment W is binary and unconfounded, we have tau (X) = E [Y (1) - Y (0) | X = x], where Y (0) and Y (1) are potential outcomes corresponding to the two possible treatment states.
Value
A trained causal forest object. If tune.parameters is enabled, then tuning information will be included through the 'tuning.output' attribute.