- Machine Learning & Causal Inference🔍
- Frameworks for estimating causal effects in observational settings🔍
- Machine Learning for Estimating Causal Effects🔍
- causal|ml/README.md at master🔍
- Doubly Robust Triple Cross|Fit Estimation for Causal Inference with ...🔍
- Causal mediation analysis with double machine learning🔍
- Causal Machine Learning and its use for public policy🔍
- Double/Debiased Machine Learning for Dynamic Treatment Effects🔍
Estimating Causal Effects with Double Machine Learning
Machine Learning & Causal Inference: A Short Course
An introduction to estimation of average treatment effects using data from randomized controlled trials or in settings where the unconfoundedness assumption ...
Frameworks for estimating causal effects in observational settings
Another important contribution of our tutorial is that we cover the recent trend of using machine learning (ML) for causal inference such as ...
Machine Learning for Estimating Causal Effects - August 2024
When used properly, these tools have tremendous potential to yield robust effect estimates with minimal assumptions. However, both machine ...
ddml: Double/Debiased Machine Learning in Stata
Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables ...
causal-ml/README.md at master - GitHub
Susan Athey, Guido Imbens. Machine Learning Methods for Estimating Heterogeneous Causal Effects, ArXiv, 2015. ... Double/Debiased Machine Learning for Treatment ...
Doubly Robust Triple Cross-Fit Estimation for Causal Inference with ...
A widely adopted approach to estimating causal effects is the double robust (DR) estimator [2], which effectively addresses potential ...
Causal mediation analysis with double machine learning - OUCI
SummaryThis paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional ...
Causal Machine Learning and its use for public policy
These two comprehensive estimation principles are double/debiased Machine Learning (DML) and causal forests (CF). While DML uses predictive ...
Double/Debiased Machine Learning for Dynamic Treatment Effects
We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future ...
Estimating Causal Effects using a Multi-task Deep Ensemble.
A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, ...
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given ...
Inferring heterogeneous treatment effects of crashes on highway traffic
A doubly robust causal machine learning framework is proposed for estimating causal effect of traffic crashes on speed reduction.
Causal Machine Learning with CausalELM :: Juliacon 2024 - pretalx
However, estimating causal effects is not easy. Running A/B tests or randomized control trials are expensive: researchers do not have the ...
Exploring modern machine learning methods to improve causal ...
Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive ...
Double machine learning and automated confounder selection
... machine learning algorithms for automated variable selection in a causal inference setting. ... causal effect estimates. In fact, the advantage ...
(2018). 12Sample-splitting procedures are frequently used in causal machine learning literature including Double Machine Learning. (Chernozhukov et al., ...
Be careful when interpreting predictive models in search of causal ...
One particularly flexible tool for observational causal inference is double/debiased machine learning. ... double ML effect estimate from econML as a line.
Causal inference/Treatment effects - Stata
Stata's causal-inference suite allows you to estimate experimental-type causal effects from observational data.
Using Causal Inference and Double Machine Learning to Uncover ...
This study rigorously estimates the causal effect of curricular complexity on four-year graduation rates across 26 universities in the United ...
10 Causality - Supervised Machine Learning for Science
Unfortunately, observational data alone does not provide causal insight [6]. Insight. It is impossible to distinguish causes from effects from observational ...