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Questions tagged [doubly-robust-estimator]

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Is including weights in g-computation not the same as a plug-in doubly robust estimator?

In the R package vignette for WeightIt(), in the section "Modeling the Outcome", it explains that (assuming I'm reading correctly) that the purpose of applying g-computation after creating ...
user11513145's user avatar
2 votes
0 answers
20 views

Semi-parameteric estimation

I am interested in the effect of certain interventions $T$ on my value of interest $Y$, my model is, $$Y = \tau f(T, X, Z) + g(X, Z), $$ where $f(T, X, Z) = T \times X + T \times Z$ , that is all the ...
Kozolovska's user avatar
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1 vote
0 answers
567 views

How does double machine learning differ from doubly robust estimation?

I am working on estimating the causal effect of a binary treatment T on a binary outcome y, from observational data. I have access to features X and W, which presumably have affected y and T, ...
bjarkemoensted's user avatar
2 votes
1 answer
84 views

Bang and Robins doubly-robust estimator biased and with large variance?

In their 2005 paper (also see the correction here) Bang and Robins describe a doubly robust estimator of the average treatment effect. In short, the procedure is: Estimate inverse probability of ...
Lachlan's user avatar
  • 1,192
0 votes
1 answer
553 views

Inverse Probability Weighting for Binary Outcomes

When using IPW with a binary outcome, the results after IPW are not bound between 0 and 1. I am using IPW to get doubly robust scores. $$ \mu + \frac{D_i(y_i - \mu)}{ps_i} $$ where $\mu$ is the ...
jkortner's user avatar
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3 votes
1 answer
1k views

Adjusting the model by propensity scores after propensity score matching

I want to control multiple confounders in my data, and I have noticed that including the propensity scores as a variable in the model gives good performance after propensity score matching. I know ...
elkadi's user avatar
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6 votes
0 answers
545 views

Derivation of a doubly robust estimator with clever covariate and inverse probability weighting

With notation: outcome $Y$, (binary) treatment $A$, and covariates $L$. In Hernan and Robins (2020) causal inference textbook: To obtain a doubly robust estimate of the average causal effect, first ...
Randel's user avatar
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4 votes
2 answers
1k views

Doubly robust learning with binary treatment and outcome

I'm trying to use doubly robust learning to estimate heterogenous treatment effects. My treatments T and outcomes y are both binary. I'm following the example listed under "How do I select the ...
bjarkemoensted's user avatar
1 vote
0 answers
310 views

Doubly robust learning with same features influencing treatment and outcome

I'm looking at some of the examples in the econML package for double machine learning. Specifically, the example found here (code below). In the example W is the features which might influence both ...
bjarkemoensted's user avatar
7 votes
1 answer
457 views

Is the emmeans R package performing causal inference G-computation?

So I am trying to get an understanding of causal inference and how it differs from the usual contrasts. I regularly use the emmeans package in R, and I am wondering when the function emmeans() ...
Vattaka's user avatar
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1 vote
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Evaluation metrics for an RL model. How to select then?

I trained an RL model adapting the RL batch example (Jupyter Notebook) to the problem I was aiming to solve. As for the training, everything went well but, even though the RL batch returned several ...
Adriano's user avatar
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1 vote
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215 views

Doubly Robust Estimator

When use Doubly Robust Estimator we train m0/m1 models and propensity score model to be used by the estimator. Is it OK to use the same dataset to train those models and then use them to measure ATE ...
Dennis Lyubyvy's user avatar
8 votes
1 answer
518 views

Variance for a doubly-robust CATE estimator

I am interested in how the variance for the conditional average treatment effect (CATE) is calculated for the doubly robust pseudo-outcome approach. Below are the exact details of the problem and my ...
pzivich's user avatar
  • 2,572
2 votes
1 answer
133 views

Problem to implement Bang & Robins double robust estimator

I have a question with regard to the implementation of Bang & Robins (2005) double robust estimator of a treatment effect (formula at the bottom of page 964). The idea of their estimator is to ...
Stefan's user avatar
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10 votes
1 answer
2k views

Theory behind Targeted Maximum Likelihood Estimation (TMLE)

There are many fine how-to articles describing how to implement TMLE but they avoid the details of the underlying theory. I'm currently working my way through Targeted Learning: Causal Inference for ...
RobertF's user avatar
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3 votes
1 answer
571 views

Does a doubly robust estimator magnify bias if *both* the outcome regression and inverse propensity score weighting are incorrect models?

The doubly robust estimator is a popular method for measuring the average treatment effect with observational data (assuming no unmeasured confounders): $$ \hat{\Delta}_{DR} = n^{-1}\sum_{i=1}^n \...
RobertF's user avatar
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