Double Machine Learning for Causal Inference

Causal mediation analysis with double machine learning

The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are ...

An Object-Oriented Implementation of Double Machine Learning in R

Machine Learning. Causal Inference. Causal Machine Learning. R. mlr3. Object Orientation. You can help us to improve our editorial management ...

Causal hybrid modeling with double machine learning ... - IOPscience

This paper introduces a novel approach to estimating hybrid models via a causal inference framework, specifically employing double machine learning (DML) to ...

DoubleML: Double Machine Learning in R

Internal data object that implements the causal model as specified by the user via y_col, ... Program Evaluation and. Causal Inference With High- ...

Tutorial on Causal Inference and its Connections to Machine ...

We show an example of using Propensity Score Stratification using DoWhy, and a machine learning-based method called Double-ML using EconML. [6]:. # III ...

Lecture 2. Double Machine Learning for Causal and Treatment Effects

and provably valid (asymptotic) inference for causal parameters, using a wide class of flexible (ML, nonparametric) methods to fit the nuisance parameters.

Double ML - Causal Wizard

... Causal Inference methods and machine learning tools, in an online web-app software. Causal Wizard provides graphical causal software toosl for causal ML ...

Machine Learning and Causal Inference - Mixtape Sessions

... causal inference methods like double machine learning (DML) and post-double selection lasso (PDS lasso). The course covers the conceptual and theoretical ...

A Double Machine Learning Approach to Combining Experimental

When only one of these assumptions is violated, we provide semiparametrically efficient treatment effect estimators. However, our no-free-lunch theorem ...

DoubleML - An Object-Oriented Implementation of Double Machine ...

It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods ...

Double Machine Learning for Causal and Treatment Effects - YouTube

Victor Chernozhukov of the Massachusetts Institute of Technology provides a general framework for estimating and drawing inference about a ...

double machine learning | Chen Xing

double machine learning. Study Notes on Bounding OVB in Causal ML · Motivation In empirical research, one of the challenges to causal inference is ...

ML_Lecture08_doubleML - Jeff Thurk

... machine learning and causal inference of the last decade. For example, tech ... The double-debiased machine learning model implicitly assumes that the ...

A Causal Analysis of Market Contagion: A Double Machine Learning ...

Although there are various theories of economic causality, there has not yet been a wide adoption of machine learning techniques for causal inference within ...

ddml: Double/Debiased Machine Learning in Stata

causal inference in common econometric models, Chernozhukov et al. (2018) propose. Double/Debiased Machine Learning (DDML), which exploits Neyman orthogonality ...

Causal Forests Double ML example using EconML - Kaggle

The gold standard when it comes to studying cause and effect is the experiment. In the field of machine learning such experiments often take the form of A/B ...

NeurIPS19 CausalML

NeurIPS 2019 Workshop. “Do the right thing”: machine learning and causal inference for improved decision making. December 14 ...

Estimating Marketing Component Effects: Double Machine Learning ...

In the first part of the analysis, we choose an email as baseline and estimate the pairwise causal treatment effect parameters with respect to ...

Double/debiased Machine Learning for Causal Inference on ...

This paper discusses the use of double/debiased machine learning (DML) for es- timating the average treatment effect (ATE) on a survival function using ...

Machine Learning & Causal Inference: A Short Course

This accompanying tutorial introduces key concepts in machine learning-based causal inference, and can be used as both lecture notes and as programming examples ...