Selection bias

60 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

PanNuke Dataset Extension, Insights and Baselines

TIA-Lab/PanNuke-metrics 24 Mar 2020

The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

shenweichen/DeepCTR 21 Apr 2018

To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.

A Debiased MDI Feature Importance Measure for Random Forests

shifwang/paper-debiased-feature-importance NeurIPS 2019

Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones.

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

arthua196/Leakage-Neutral-Learning-for-QuoraQP ACL 2019

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

HarrieO/OnlineLearningToRank 15 Jul 2019

At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.

Automated Dependence Plots

davidinouye/adp 2 Dec 2019

To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.

Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules

rfgong/IVaps 26 Apr 2021

We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic decision-making algorithms.

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

vanderschaarlab/mlforhealthlabpub NeurIPS 2017

Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).

Deep Counterfactual Networks with Propensity-Dropout

Shantanu48114860/Deep-Counterfactual-Networks-with-Propensity-Dropout 19 Jun 2017

The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.

Target contrastive pessimistic risk for robust domain adaptation

wmkouw/libTLDA 25 Jun 2017

In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain.