Tootfinder

Integrating representative and non-representative survey data for efficient inference
Nathaniel Dyrkton, Paul Gustafson, Harlan Campbell
https://arxiv.org/abs/2404.02283

Integrating representative and non-representative survey data for efficient inference
Non-representative surveys are commonly used and widely available but suffer from selection bias that generally cannot be entirely eliminated using weighting techniques. Instead, we propose a Bayesian method to synthesize longitudinal representative unbiased surveys with non-representative biased surveys by estimating the degree of selection bias over time. We show using a simulation study that synthesizing biased and unbiased surveys together out-performs using the unbiased surveys alone, even…

A First Look at Selection Bias in Preference Elicitation for Recommendation
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
https://arxiv.org/abs/2405.00554

A First Look at Selection Bias in Preference Elicitation for Recommendation
Preference elicitation explicitly asks users what kind of recommendations they would like to receive. It is a popular technique for conversational recommender systems to deal with cold-starts. Previous work has studied selection bias in implicit feedback, e.g., clicks, and in some forms of explicit feedback, i.e., ratings on items. Despite the fact that the extreme sparsity of preference elicitation interactions make them severely more prone to selection bias than natural interactions, the effe…

This https://arxiv.org/abs/2401.04190 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Is it possible to know cosmological fine-tuning?
Fine-tuning studies whether some physical parameters, or relevant ratios between them, are located within so-called life-permitting intervals of small probability outside of which carbon-based life would not be possible. Recent developments have found estimates of these probabilities that circumvent previous concerns of measurability and selection bias. However, the question remains if fine-tuning can indeed be known. Using a mathematization of the epistemological concepts of learning and knowl…

This https://arxiv.org/abs/2401.04190 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Is it possible to know cosmological fine-tuning?
Fine-tuning studies whether some physical parameters, or relevant ratios between them, are located within so-called life-permitting intervals of small probability outside of which carbon-based life would not be possible. Recent developments have found estimates of these probabilities that circumvent previous concerns of measurability and selection bias. However, the question remains if fine-tuning can indeed be known. Using a mathematization of the epistemological concepts of learning and knowl…

This https://arxiv.org/abs/2208.06039 has been replaced.
link: https://scholar.google.com/scholar?q=a

Semiparametric adaptive estimation under informative sampling
In survey sampling, survey data do not necessarily represent the target population, and the samples are often biased. However, information on the survey weights aids in the elimination of selection bias. The Horvitz-Thompson estimator is a well-known unbiased, consistent, and asymptotically normal estimator; however, it is not efficient. Thus, this study derives the semiparametric efficiency bound for various target parameters by considering the survey weight as a random variable and consequent…

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
Yoichi Chikahara, Kansei Ushiyama
https://arxiv.org/abs/2404.17483

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but are predictive of potential outcomes. To avoid losing such predictive feature information, existing methods learn separate feature representa…

Sample-Efficient Linear Regression with Self-Selection Bias
Jason Gaitonde, Elchanan Mossel
https://arxiv.org/abs/2402.14229 https://…

Sample-Efficient Linear Regression with Self-Selection Bias
We consider the problem of linear regression with self-selection bias in the unknown-index setting, as introduced in recent work by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [STOC 2023]. In this model, one observes $m$ i.i.d. samples $(\mathbf{x}_{\ell},z_{\ell})_{\ell=1}^m$ where $z_{\ell}=\max_{i\in [k]}\{\mathbf{x}_{\ell}^T\mathbf{w}_i+η_{i,\ell}\}$, but the maximizing index $i_{\ell}$ is unobserved. Here, the $\mathbf{x}_{\ell}$ are assumed to be $\mathcal{N}(0,I_n)$ and the noise …

This https://arxiv.org/abs/2402.01740 has been replaced.
link: https://scholar.google.com/scholar?q=a

Compensatory Biases Under Cognitive Load: Reducing Selection Bias in Large Language Models
Large Language Models (LLMs) like gpt-3.5-turbo and claude-instant-1.2 have become instrumental in interpreting and executing semantic-based tasks. Unfortunately, these models' inherent biases, akin to human cognitive biases, adversely affect their performance. Particularly affected is object selection from lists; a fundamental operation in digital navigation and decision-making. This research critically examines these biases and quantifies the effects on a representative list selection task. T…

Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2402.18185

Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may…

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
A. Liu, E. Bulbul, T. Shin, A. von der Linden, V. Ghirardini, M. Kluge, J. S. Sanders, S. Grandis, X. Zhang, E. Artis, Y. E. Bahar, F. Balzer, N. Clerc, N. Malavasi, A. Merloni, K. Nandra, M. E. Ramos-Ceja, S. Zelmer
https://arxiv.org/…

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
We explore halo assembly bias on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we construct two subsamples of galaxy clusters which consist of supercluster members (SC) and isolated clusters (ISO) respectively. After correcting the selection effects on redshift, mass, and survey depth, we compute the excess in the concentration of the intracluster gas of isolated …

This https://arxiv.org/abs/2303.09205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Addressing bias in online selection with limited budget of comparisons
Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them otherwise. The latter case requires additional costly assessments, leading to a potentially high total cost for the hiring organization. Given an assigned budget, what would be an optimal strategy to select the most qualified candidate? We model the above problem as a multicolor secretary problem, allowing comparisons be…

This https://arxiv.org/abs/2203.12572 has been replaced.
link: https://scholar.google.com/scholar?q=a

Post-selection inference for e-value based confidence intervals
Suppose that one can construct a valid $(1-δ)$-confidence interval (CI) for each of $K$ parameters of potential interest. If a data analyst uses an arbitrary data-dependent criterion to select some subset $S$ of parameters, then the aforementioned CIs for the selected parameters are no longer valid due to selection bias. We design a new method to adjust the intervals in order to control the false coverage rate (FCR). The main established method is the "BY procedure" by Benjamini and Yekutieli …

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
A. Liu, E. Bulbul, T. Shin, A. von der Linden, V. Ghirardini, M. Kluge, J. S. Sanders, S. Grandis, X. Zhang, E. Artis, Y. E. Bahar, F. Balzer, N. Clerc, N. Malavasi, A. Merloni, K. Nandra, M. E. Ramos-Ceja, S. Zelmer
https://arxiv.org/…

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
We explore halo assembly bias on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we construct two subsamples of galaxy clusters which consist of supercluster members (SC) and isolated clusters (ISO) respectively. After correcting the selection effects on redshift, mass, and survey depth, we compute the excess in the concentration of the intracluster gas of isolated …

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Jiacong Du, Xu Shi, Donglin Zeng, Bhramar Mukherjee
https://arxiv.org/abs/2403.18039

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Causal inference on the average treatment effect (ATE) using non-probability samples, such as electronic health records (EHR), faces challenges from sample selection bias and high-dimensional covariates. This requires considering a selection model alongside treatment and outcome models that are typical ingredients in causal inference. This paper considers integrating large non-probability samples with external probability samples from a design survey, addressing moderately high-dimensional conf…

Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows
Evan D. Cook, Marc-Antoine Lavoie, Steven L. Waslander
https://arxiv.org/abs/2402.06537

Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows
Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting. In this work, we investigate the use of feature density estimation via normalizing flows for OOD detection and present a fully unsupervised approach which requires no exposure to OOD data, avoiding researcher bias in OOD sample selection. This is a post-hoc method which can be applied to any pretrained model, and involves training a lightweight auxiliary normalizing flow mod…

Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media
Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo, Huan Liu
https://arxiv.org/abs/2403.04009

Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media
News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical evidence supporting their efficacy. In this paper, we analyze: (i) how inherent vaccine stances subtly influence individuals' selection of news sources and participation in social media discussions; and (ii) the impact of exposure to PBN on users' at…

This https://arxiv.org/abs/2303.09205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Addressing bias in online selection with limited budget of comparisons
Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them otherwise. The latter case requires additional costly assessments, leading to a potentially high total cost for the hiring organization. Given an assigned budget, what would be an optimal strategy to select the most qualified candidate? We model the above problem as a multicolor secretary problem, allowing comparisons be…

Jet Suppression and Azimuthal Anisotropy from RHIC to LHC
Yacine Mehtar-Tani, Daniel Pablos, Konrad Tywoniuk
https://arxiv.org/abs/2402.07869 https://

Jet Suppression and Azimuthal Anisotropy from RHIC to LHC
Azimuthal anisotropies of high-$p_T$ particles produced in heavy-ion collisions are understood as an effect of a geometrical selection bias. Particles oriented in the direction in which the QCD medium formed in these collisions is shorter, suffer less energy loss, and thus, are over-represented in the final ensemble compared to those oriented in the direction in which the medium is longer. In this work we present the first semi-analytical predictions, including propagation through a realistic, …

This https://arxiv.org/abs/2208.02657 has been replaced.
link: https://scholar.google.com/scholar?q=a

Using Instruments for Selection to Adjust for Selection Bias in Mendelian Randomization
Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing da…

A3COSMOS & A3GOODSS: Continuum Source Catalogues and Multi-band Number Counts
Sylvia Adscheid, Benjamin Magnelli, Daizhong Liu, Frank Bertoldi, Ivan Delvecchio, Carlotta Gruppioni, Eva Schinnerer, Alberto Traina, Matthieu B\'ethermin, Athanasia Gkogkou
https://arxiv.org/abs/2403.03125 https://arxiv.org/pdf/2403.03125
arXiv:2403.03125v1 Announce Type: new
Abstract: Galaxy submillimetre number counts are a fundamental measurement in our understanding of galaxy evolution models. Most early measurements are obtained via single-dish telescopes with substantial source confusion, whereas recent interferometric observations are limited to small areas. We used a large database of ALMA continuum observations to accurately measure galaxy number counts in multiple (sub)millimetre bands, thus bridging the flux density range between single-dish surveys and deep interferometric studies. We continued the Automated Mining of the ALMA Archive in the COSMOS Field project (A3COSMOS) and extended it with observations from the GOODS-South field (A3GOODSS). The database consists of ~4,000 pipeline-processed continuum images from the public ALMA archive, yielding 2,050 unique detected sources. To infer galaxy number counts, we constructed a method to reduce the observational bias inherent to targeted pointings that dominate the database. This method comprises a combination of image selection, masking, and source weighting. The effective area was calculated by accounting for inhomogeneous wavelengths, sensitivities, and resolutions and for spatial overlap between images. We tested and calibrated our method with simulations. We obtained the first number counts derived in a consistent and homogeneous way in four different ALMA bands covering a relatively large area. The results are consistent with number counts from the literature within the uncertainties. We extended the available depth in ALMA Band 4 by 0.4 dex with respect to previous studies. In Band 7, at the depth of the inferred number counts, ~40% of the cosmic infrared background is resolved into discrete sources. This fraction, however, decreases with wavelength, reaching ~4% in Band 3. Finally, we used the number counts to test models of dusty galaxy evolution, and find a good agreement within the uncertainties.

Assessing COVID-19 Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Justin B. DeMonte, Bonnie E. Shook-Sa, Michael G. Hudgens
https://arxiv.org/abs/2403.18115

Assessing COVID-19 Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Observational data are often used to estimate real-world effectiveness and durability of coronavirus disease 2019 (COVID-19) vaccines. A sequence of nested trials can be emulated to draw inference from such data while minimizing selection bias, immortal time bias, and confounding. Typically, when nested trial emulation (NTE) is employed, effect estimates are pooled across trials to increase statistical efficiency. However, such pooled estimates may lack a clear interpretation when the treatment…

This https://arxiv.org/abs/2307.04527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Automatic Debiased Machine Learning for Covariate Shifts
In this paper we address the problem of bias in machine learning of parameters following covariate shifts. Covariate shift occurs when the distribution of input features change between the training and deployment stages. Regularization and model selection associated with machine learning biases many parameter estimates. In this paper, we propose an automatic debiased machine learning approach to correct for this bias under covariate shifts. The proposed approach leverages state-of-the-art techn…

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Jaron J. R. Lee, AmirEmad Ghassami, Ilya Shpitser
https://arxiv.org/abs/2404.06602

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data on the superpopulation of interest. Such data may measure a different set of variables, or be obtained under different experimental conditions than the primary dataset. Analysis based on multiple datasets must carefully account for similarities between datasets, while appropriately accounting for differences. In add…

This https://arxiv.org/abs/2404.06602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data on the superpopulation of interest. Such data may measure a different set of variables, or be obtained under different experimental conditions than the primary dataset. Analysis based on multiple datasets must carefully account for similarities between datasets, while appropriately accounting for differences. In add…

This https://arxiv.org/abs/2404.02283 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Integrating representative and non-representative survey data for efficient inference
Non-representative surveys are commonly used and widely available but suffer from selection bias that generally cannot be entirely eliminated using weighting techniques. Instead, we propose a Bayesian method to synthesize longitudinal representative unbiased surveys with non-representative biased surveys by estimating the degree of selection bias over time. We show using a simulation study that synthesizing biased and unbiased surveys together out-performs using the unbiased surveys alone, even…

Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
Ying Jin, Zhimei Ren
https://arxiv.org/abs/2403.03868 https://

Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
Conformal prediction builds marginally valid prediction intervals which cover the unknown outcome of a randomly drawn new test point with a prescribed probability. In practice, a common scenario is that, after seeing the test unit(s), practitioners decide which test unit(s) to focus on in a data-driven manner, and wish to quantify the uncertainty for the focal unit(s). In such cases, marginally valid prediction intervals for these focal units can be misleading due to selection bias. This paper …

This https://arxiv.org/abs/2310.17999 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Automated threshold selection and associated inference uncertainty for univariate extremes
Threshold selection is a fundamental problem in any threshold-based extreme value analysis. While models are asymptotically motivated, selecting an appropriate threshold for finite samples is difficult and highly subjective through standard methods. Inference for high quantiles can also be highly sensitive to the choice of threshold. Too low a threshold choice leads to bias in the fit of the extreme value model, while too high a choice leads to unnecessary additional uncertainty in the estimati…

This https://arxiv.org/abs/2308.05577 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Optimal Designs for Two-Stage Inference
The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of this first stage is influenced by three components: (1) main effect estimators' variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this paper, we propose a design criterion based on expected…

This https://arxiv.org/abs/2308.05577 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Optimal Designs for Two-Stage Inference
The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of this first stage is influenced by three components: (1) main effect estimators' variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this paper, we propose a design criterion based on expected…

Assumption-Lean Quantile Regression
Georgi Baklicharov, Christophe Ley, Vanessa Gorasso, Brecht Devleesschauwer, Stijn Vansteelandt
https://arxiv.org/abs/2404.10495

Assumption-Lean Quantile Regression
Quantile regression is a powerful tool for detecting exposure-outcome associations given covariates across different parts of the outcome's distribution, but has two major limitations when the aim is to infer the effect of an exposure. Firstly, the exposure coefficient estimator may not converge to a meaningful quantity when the model is misspecified, and secondly, variable selection methods may induce bias and excess uncertainty, rendering inferences biased and overly optimistic. In this paper…

This https://arxiv.org/abs/2208.07614 has been replaced.
link: https://scholar.google.com/scholar?q=a

Reweighting the RCT for generalization: finite sample error and variable selection
Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain characteristics compared to the target population, for which one wants conclusions on treatment effectiveness. Re-weighting trial individuals to match the target population can improve the treatment effect estimation. In this work, we establish the exact expressions of the bias and variance of such reweighting procedures -- …