Tootfinder

Copas-Jackson-type bounds for publication bias over a general class of selection models
Taojun Hu, Yi Zhou, Xiao-Hua Zhou, Satoshi Hattori
https://arxiv.org/abs/2508.17716 https…

Copas-Jackson-type bounds for publication bias over a general class of selection models
Publication bias (PB) is one of the most vital threats to the accuracy of meta-analysis. Adjustment or sensitivity analysis based on selection models, which describe the probability of a study being published, provide a more objective evaluation of PB than widely-used simple graphical methods such as the trim-and-fill method. Most existing methods rely on parametric selection models. The Copas-Jackson bound (C-J bound) provides a worst-case bound of an analytical form over a nonparametric class…

from my link log —
Toxic Origins, Toxic Decisions: bias in CEO selection towards risk-taking, analysed using polluted Superfund sites.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5270031
saved 2025-05-31

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Dariia Puhach, Amir H. Payberah, \'Eva Sz\'ekely
https://arxiv.org/abs/2508.13603 h…

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Similar to text-based Large Language Models (LLMs), Speech-LLMs exhibit emergent abilities and context awareness. However, whether these similarities extend to gender bias remains an open question. This study proposes a methodology leveraging speaker assignment as an analytic tool for bias investigation. Unlike text-based models, which encode gendered associations implicitly, Speech-LLMs must produce a gendered voice, making speaker selection an explicit bias cue. We evaluate Bark, a Text-to-Sp…

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Mengke Li, Yukun Liu, Pengfei Li, Jing Qin
https://arxiv.org/abs/2507.13615

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Meta analysis is commonly-used to synthesize multiple results from individual studies. However, its validation is usually threatened by publication bias and between-study heterogeneity, which can be captured by the Copas selection model. Existing inference methods under this model are all based on conditional likelihood and may not be fully efficient. In this paper, we propose a full likelihood approach to meta analysis by integrating the conditional likelihood and a marginal semi-parametric em…

Note on Selection Bias in Observational Estimates of Algorithmic Progress
Parker Whitfill
https://arxiv.org/abs/2508.11033 https://arxiv.org/pdf/2508.11033…

Note on Selection Bias in Observational Estimates of Algorithmic Progress
Ho et. al (2024) is an interesting paper that attempts to estimate the degree of algorithmic progress from language models. They collect observational data on language models' loss and compute over time, and argue that as time has passed, language models' algorithmic efficiency has been rising. That is, the loss achieved for fixed compute has been dropping over time. In this note, I want to raise one potential methodological problem with the estimation strategy. Intuitively, if part of algorith…

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
Richard Wyss, Ben B. Hansen, Georg Hahn, Lars van der Laan, Joshua K. Lin
https://arxiv.org/abs/2506.17760

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection operator (LASSO) is a data-adaptive prediction algorithm that has become the most widely used tool for large-scale PS estimation in these settings. However, recent work has shown that the use of data-adaptive algorithms for PS estimation can come at the cost of slow convergence rates, resulting in PS-based causal estimator…

Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection
Jiangen He
https://arxiv.org/abs/2508.02740 https://arxiv.org/pdf/2508.02740

Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection
Large language models (LLMs) are rapidly being adopted as research assistants, particularly for literature review and reference recommendation, yet little is known about whether they introduce demographic bias into citation workflows. This study systematically investigates gender bias in LLM-driven reference selection using controlled experiments with pseudonymous author names. We evaluate several LLMs (GPT-4o, GPT-4o-mini, Claude Sonnet, and Claude Haiku) by varying gender composition within c…

Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Yuki Ohnishi, Michael O. Harhay, Fan Li
https://arxiv.org/abs/2506.19015

Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Recurrent events often serve as key endpoints in clinical studies but may be prematurely truncated by terminal events such as death, creating selection bias and complicating causal inference. To address this challenge, we propose novel causal estimands within the principal stratification framework, introducing a refined ``always-survivor'' stratum that defines survival until the final recurrent event rather than a fixed time point, yielding more stable and interpretable causal contrasts. We dev…

Selection bias effects on high-$p_\mathrm{T}$ yield and correlation measurements in Oxygen Oxygen collisions
JaeBeom Park, J. L. Nagle, Dennis V. Perepelitsa, Sanghoon Lim, Constantin Loizides
https://arxiv.org/abs/2507.03603

Selection bias effects on high-$p_\mathrm{T}$ yield and correlation measurements in Oxygen+Oxygen collisions
Oxygen+Oxygen (O+O) collisions at RHIC and the LHC offer a unique experimental opportunity to observe the onset of jet quenching in intermediate relativistic collision systems. As with the smaller proton-nucleus or larger nucleus-nucleus systems, measurements of centrality-selected high-$p_\mathrm{T}$ processes in O+O collisions are expected to be sensitive to selection bias effects, which will be necessary to quantify or mitigate before a definitive conclusion on the presence of jet quenching.…

Tree-based methods for length-biased survival data
Jinwoo Lee, Jiyu Sun, Hyunwoo Lee, Donghwan Lee
https://arxiv.org/abs/2508.16312 https://arxiv.org/pdf/2…

Tree-based methods for length-biased survival data
Left-truncated survival data commonly arise in prevalent cohort studies, where only individuals who have experienced disease onset and survived until enrollment in the study. When the onset process follows a stationary Poisson process, the resulting data are length-biased. This sampling mechanism induces a selection bias towards longer survival individuals, and nonparametric and semiparametric methods for traditional survival data are not directly applicable. While tree-based methods developed …

Identification of Causal Effects with a Bunching Design
Carolina Caetano, Gregorio Caetano, Leonard Goff, Eric Nielsen
https://arxiv.org/abs/2507.05210 htt…

Identification of Causal Effects with a Bunching Design
We show that causal effects can be identified when there is bunching in the distribution of a continuous treatment variable, without imposing any parametric assumptions. This yields a new nonparametric method for overcoming selection bias in the absence of instrumental variables, panel data, or other popular research designs for causal inference. The method leverages the change of variables theorem from integration theory, relating the selection bias to the ratio of the density of the treatment…

"CEOs born in future Superfund sites are more likely to be promoted internally, suggesting firms reward observed success without recognizing underlying risk tolerance."
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5270031

New Insights into the T Tauri Binary Separation Distribution
Caleb Eastlund, Maxwell Moe, Kaitlin M. Kratter, Marina Kounkel
https://arxiv.org/abs/2506.07938

New Insights into the T Tauri Binary Separation Distribution
For three decades, adaptive optic surveys have revealed an excess of T Tauri binaries across a = 10-100 au in nearby star-forming regions compared to the field population of main-sequence (MS) stars. Such an excess requires that most stars are born in dense clusters and subjected to significant dynamical processing that disrupts such binaries across intermediate separations. However, we demonstrate that the apparent excess is due to an observational selection bias. Close binaries within a < 100…

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Rikuta Hamaya, Etsuji Suzuki, Konan Hara
https://arxiv.org/abs/2506.12296

Generalizable estimation of conditional average treatment effects using Causal Forest in randomized
Generalizing conditional average treatment effects (CATE) estimates in a randomized controlled trial (RCT) to a broader source population can be challenging because of selection bias and high-dimensional covariates. We aim to evaluate CATE estimation approaches using Causal Forest that address selection bias due to trial participation. We propose and compare four CATE estimation approaches using Causal Forest: (1) ignoring selection variables, (2) including selection variables, (3) using invers…

Recover Experimental Data with Selection Bias using Counterfactual Logic
Jingyang He, Shuai Wang, Ang Li
https://arxiv.org/abs/2506.00335 https://

Recover Experimental Data with Selection Bias using Counterfactual Logic
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased observational and interventional distributions from biased data using partial external information, the complexity of the backdoor adjustment and the method's strong reliance on observational data limit its applicability in many practical settings. In this paper, we formally di…

This https://arxiv.org/abs/2107.02637 has been replaced.
link: https://scholar.google.com/scholar?q=a

Difference-in-Differences with a Continuous Treatment
This paper analyzes difference-in-differences designs with a continuous treatment. We show that treatment effect on the treated-type parameters can be identified under a generalized parallel trends assumption that is similar to the binary treatment setup. However, interpreting differences in these parameters across different values of the treatment can be particularly challenging due to selection bias that is not ruled out by the parallel trends assumption. We discuss alternative, typically str…

Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis, David Sontag
https://arxiv.org/abs/2506.01191

Uncovering Bias Mechanisms in Observational Studies
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity analyses and comparisons with randomized controlled trials, or mitigating them through debiasing techniques. However, there remains a lack of methodology for uncovering the underlying mechanisms driving these biases, e.g., whether due to hidden confounding or selection of participants. In this work, we show that the r…

This https://arxiv.org/abs/2412.12405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalized entropy calibration for analyzing voluntary survey data
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first establish the relationship between the generalized calibration weighting and its dual expression for regression estimation. The dual relations…