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 ...

Estimating Causal Effects with Double Machine Learning

This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that ...

Make data-driven policies and influence decision-making - Azure ...

It estimates heterogeneous treatment effects from observational data via the double machine learning technique. Use causal inference when you ...

Double machine learning for treatment and causal parameters - IFS

... inference on the main parameter in a partially linear regression model and estimation and inference on average treatment eff ects and ...

Machine Learning for Economists: Part 5 – [Causal] Inference

Post-lasso double selection strategy that 'immunizes' estimation from selection errors exists. . . Page 39. Double-Selection Lasso: Instrumental Variables (IV).

Double Machine Learning Density Estimation for Local Treatment ...

The LTE measures the causal effect among compliers, which usually comes under the assumption of monotonicity (only the ones offered the treatment are allowed to ...

Robust Causal Inference using Double/Debiased Machine Learning

Robust Causal Inference using Double/Debiased Machine Learning: A Guide for Empirical Research. 180 views · 3 weeks ago ...more. MZES Methods ...

Controlling for Text in Causal Inference with Double Machine Learning

Controlling for Text in Causal Inference with Double Machine Learning ... Abstract: Text plays an increasingly important role in the study of ...

Double/Debiased Machine Learning for Economists: Practical ...

... causal inferences. Keywords: Double/Debiased Machine Learning, Causal Inference, Econometrics, Model Misspecification, High-Dimensional Data.

Using double-debiased machine learning to estimate the impact of ...

Abstract. Machine learning approaches provide an alternative approach to traditional fixed effects estimators in causal inference. In particular ...

Doubly robust learning for causal inference - Dan MacKinlay

To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of ...

Causal Machine Learning and its use for public policy

Causal mediation analysis with double machine learning. ... Machine learning for causal inference: estimating heterogeneous treatment effects.

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data

... causal inference and treatment effect estimation. We propose a neural ... double machine learning (DML) framework, specifically the partially linear model.

ddml: Double/Debiased Machine Learning in Stata | IZA

... machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation. Download. Keywords. st0001 · causal inference · machine ...

An Adaptive Deep Learning for Causal Inference Based on Support ...

Three estimators were applied for double/debiased machine learning causal inference a paradigm that estimates the causal treatment effect from observational ...

Estimating Causal Effects with Double Machine Learning

By combining multiple models in a clever way, it can provide more accurate estimates of the true causal effect, even in complex, real-world ...

Machine learning in the estimation of causal effects - PubMed

... causal effects: targeted minimum loss-based estimation and double/debiased machine learning ... causal inference using machine learning. To my ...

Machine learning in causal inference for epidemiology

Augmented inverse probability weighting and double/debiased machine learning. AIPW, first proposed by Robins and colleagues [32] and further ...

Variable selection in double/debiased machine learning for causal ...

... machine learning in causal inference more common in recent years. The double/debiased machine learning (DML) estimator for the treatment effect ...

How Double Machine Learning Transforms Data into Decisions

... causal relationships. These assumptions, often untestable, have historically limited the effectiveness of causal inference methods. However ...