Mitigating Selection Bias: A Simulation Study of Political News Sharing and Statistical Corrections

This paper addresses a common methodological challenge in political science research: selection bias arising from the fact that we often observe only what is “chosen” or “shared,” rather than the full range of possible options. Whether scholars are studying which issues politicians emphasize, which candidates parties endorse, or which policies are adopted and publicized, focusing exclusively on the items that ultimately rise to prominence can systematically distort our understanding of the true relationships underlying political decision-making. Critically, this distortion poses a major problem to causal inference, as ignoring unselected alternatives can obscure or falsely attribute causal relationships between predictors (e.g., ideology) and outcomes (e.g., support for a particular policy or the prominence of a news story). We present a comprehensive simulation framework that systematically models both chosen and unchosen items in a political context. Although our illustrative example centers on political parties “sharing” news articles—each article bearing distinct topical, ideological, and source attributes—the approach applies broadly to any process where a subset of items is selected from a larger pool, including policy proposals, campaign endorsements, and media strategy. By varying party-level attributes like ideology and topical ownership, as well as the availability and characteristics of potential items, the simulation reveals how restricting analysis to only observed (i.e., chosen) items can result in significant selection bias. We examine four commonly encountered sampling scenarios: Ideal (Scenario I) uses the complete “universe” of items, capturing the entire decision space of shared and unshared possibilities. Uninformed Sampling (Scenario II) randomly selects items without explicit insight into the selection process. Partial Sampling with Expansion (Scenario III) focuses on shared items but expands the data to include unshared item-party pairs, thereby restoring some missing information. Exclusive Sampling (Scenario IV) includes only the shared items, the scenario most prone to selection bias and the one most frequently encountered when researchers rely on conveniently observed data. We evaluate several strategies for correcting this bias. A basic logistic regression omitting unchosen items can yield skewed estimates and problematic causal inferences. More sophisticated methods—such as Inverse Probability Weighting (IPW), the Heckman Selection Model, and Propensity Score Matching (PSM)—seek to account for the non-random nature of observed data in different ways. Finally, we introduce a hierarchical Bayesian model that goes further by simultaneously estimating both the choice (sharing) process and its latent drivers. This model accommodates unobserved variables that may confound both the probability of selection and the outcome of interest, thereby offering a more flexible and powerful framework for correcting bias. The hierarchical Bayesian approach can more robustly capture the complexities of real-world data and yield more reliable causal inferences. Overall, our findings underscore the necessity of explicitly modeling selection mechanisms to produce valid causal claims in political science research. By demonstrating how multiple correction techniques perform under various sampling scenarios, this paper offers practical guidance for scholars dealing with partial or biased data, ultimately enhancing the validity and theoretical reach of empirical political science research.