Double Machine Learning for causal inference

Hyperparameter Tuning for Causal Inference with Double Machine ...

The relationship between the predictive performance of ML methods and the resulting causal estimation based on the Double Machine Learning (DML) approach by ...

Hyperparameter Tuning for Causal Inference with Double Machine ...

Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks.

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 ...

Hyperparameter Tuning for Causal Inference with Double Machine ...

causal estimator. In recent years, the DML framework has become popular in various disciplines, including econometrics (Knaus, 2022), reinforcement learning ( ...

Interpretable machine learning for heterogeneous treatment effect ...

... causal inference models (e.g., Double ML). In this study, we propose SAFE-TH framework to estimate and explain the heterogeneous treatment effect with ...

Using Causal Inference and Double Machine Learning to Uncover ...

The Hidden Costs of Complexity: Using Causal Inference and Double Machine Learning to Uncover Important Relationships in Higher Education Data ...

Double Machine Learning Density Estimation for Local Treatment ...

Double/Debiased Machine Learning (DML) [13]-based causal effect estimators. The DML framework has been adapted for estimating the average causal effect ...

Double Machine Learning - Vocab, Definition, and Must Know Facts

By separating the estimation of nuisance parameters from the causal effect estimation, double machine learning aims to produce more robust and reliable results.

Using Double Machine Learning to Understand Nonresponse in the ...

In contrast to predictive modeling, causal inference entails learning the effect of a particular variable on the dependent variable y while holding all other ...

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 Machine Learning Density Estimation for Local Treatment ...

of causal inference. Technical Report R-60. Causal Artificial Intelligence Laboratory, Columbia. 321. University, 2020. 322. [5] A. Belloni, V. Chernozhukov ...

An Object-Oriented Implementation of Double Machine Learning in R

DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models.

On Doubly Robust Inference for Double Machine Learning in ...

... causal effect of an exposure on an outcome. Unfortunately, exposure effect estimators that rely on machine learning predictions are generally subject to so ...

Using Double Machine Learning to Understand Nonresponse in the ...

First, like many statistical methods, the double machine learning strategy can only be applied to data sets that contain no missing values and ...

On propensity score misspecification in double/debiased machine ...

1. Machine learning methods are valuable for causal inference, particularly when dealing with high-dimensional and complex covariates. Setoguchi et al.

Double Machine Learning - Arun Subramanian's Post - LinkedIn

Double Machine Learning for Causal Inference Ever heard of the term Double Machine Learning (Double ML) and wondered…

Double Machine Learning for Causal and Treatment Effects

• ”Program Evaluation and Causal Inference with High-Dimensional ... and heterogeneous effect model (Panel A) based on orthogonal ...

Causal mediation analysis with double machine learning

Causal mediation analysis aims at decomposing the causal effect of a treatment on an outcome of interest into an indirect effect operating through a mediator ( ...

Dynamic Double Machine Learning — econml 0.15.1 documentation

It applies to the case when all potential dynamic confounders/controls (factors that simultaneously had a direct effect on the adaptive treatment decision in ...

(PDF) Anytime-Valid Inference for Double/Debiased Machine ...

PDF | Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, ...