Výsledky vyhledávání - "Rostami, Mehdi"

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Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference

Estimation of the Average Treatment Effect (ATE) is often carried out in 2 steps, wherein the first step, the treatment and outcome are modeled, and in the second step the predictions are inserted into the ATE estimator. In the first steps, numerous

Externí odkaz: http://arxiv.org/abs/2307.10536

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A Feature Selection Method that Controls the False Discovery Rate

Autor: Rostami, Mehdi, Saarela, Olli

The problem of selecting a handful of truly relevant variables in supervised machine learning algorithms is a challenging problem in terms of untestable assumptions that must hold and unavailability of theoretical assurances that selection errors are

Externí odkaz: http://arxiv.org/abs/2208.02948

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Normalized Augmented Inverse Probability Weighting with Neural Network Predictions

Autor: Rostami, Mehdi, Saarela, Olli

The estimation of Average Treatment Effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are i

Externí odkaz: http://arxiv.org/abs/2108.01768

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The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions

Autor: Rostami, Mehdi, Saarela, Olli, Escobar, Michael

The Doubly Robust (DR) estimation of ATE can be carried out in 2 steps, where in the first step, the treatment and outcome are modeled, and in the second step the predictions are inserted into the DR estimator. The model misspecification in the first

Externí odkaz: http://arxiv.org/abs/2108.00990

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