On doubly robust inference for double machine learning

[2107.06124] On doubly robust inference for double machine learning

We show that a specific implementation of the machine learning techniques can yield exposure effect estimators that have small bias even when one of the first- ...

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

(2017) differs from. 2. Page 3. Doubly Robust Inference for Double Machine Learning the usual one of n−1 times the sample variance of the efficient influence ...

On doubly robust inference for double machine learning - arXiv

Progress has been made via proposals like targeted minimum loss estimation (TMLE) and more re- cently double machine learning, which rely on ...

Is double machine learning doubly robust? If so, how?

Yes, but only because double machine learning uses a doubly robust estimator underneath the hood. There is nothing about the cross-fitting ...

(PDF) On doubly robust inference for double machine learning

Unfortunately, exposure effect estimators that rely on machine learning predictions are generally subject to so-called plug-in bias, which can ...

Journal of Machine Learning Research on X: "'On Doubly Robust ...

'On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression', by Oliver Dukes, Stijn Vansteelandt, David Whitney.

Good reading on when "doubly robust" is worth the effort?

The actual utility of doubly robust estimators arises when you want to use machine learning algorithms as part of your estimation procedure.

Double debiased machine learning nonparametric inference with ...

Using doubly robust influence function and cross-fitting, we give tractable primitive conditions under which the nuisance estimators do not affect the first- ...

Doubly Robust Learning — econml 0.15.1 documentation

Doubly Robust Learning, similar to Double Machine Learning, is a method for ... inference (confidence interval construction) for measuring the uncertainty of the ...

STA 640 — Causal Inference Chapter 3.5. Doubly Robust Estimation

double-robust estimators when “inverse probability" weights are highly variable. ... Double/debiased machine learning for treatment and structural parameters.

6.5 - Doubly Robust Methods, Matching, Double Machine ... - YouTube

6.5 - Doubly Robust Methods, Matching, Double Machine Learning, and Causal Trees. 15K views · 4 years ago ...more. Brady Neal - Causal Inference.

Estimating Identifiable Causal Effects through Double Machine ...

Doubly robust nonparametric inference on the average treatment effect. ... Double debiased machine learning nonparametric inference with continuous treatments.

Doubly Robust Inference in Causal Latent Factor Models

Doubly Robust Inference in Causal Latent Factor Models ... Double/debiased machine learning for treatment and structural parame- ters.

Estimating causal effects under sparsity using the econml package

In this post, I'll introduce the econml python package and use it to compare double machine learning and doubly robust learning.

Doubly Robust Estimation — Causal Inference for the Brave and True

21 - Meta Learners · 22 - Debiased/Orthogonal Machine Learning · 23 - Challenges with Effect Heterogeneity and Nonlinearity · 24 - The Difference-in-Differences ...

Selective machine learning of doubly robust functionals | Biometrika

Valid inference may then be obtained about the functional of interest provided that nuisance parameters can be estimated at sufficiently fast ...

Robust Covariate Selection for Doubly Robust Estimators in Causal ...

... inference collider bias double/debiased machine learning double robustness. Citations. APA. MLA. Chicago. Get more citations. Enter citation ...

Double Debiased Machine Learning Nonparametric Inference with ...

We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or ...

Double/debiased machine learning for logistic partially linear model

Our double machine learning (DML) estimator for β 0 is rated doubly robust in the same sense as Chernozhukov, Chetverikov, et al. (2018), i.e., ...

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 ...