Distributional Counterfactual Dependence in the Special Sciences
Abstract
Special-science explanations often concern distributions rather than individual outcomes. Since higher-level states depend on lower-level processes, what would make a higher-level explanation redundant? I argue that redundancy requires replacement without loss, not merely a reconstruction of individual outcomes. Distributional counterfactual dependence (DCD) identifies how changes in higher-level conditions alter an outcome distribution or a relevant feature of it. Against the background of stochastic supervenience, I distinguish this explanatory role from the dependence of higher-level states on lower-level bases. A replacement must be specified independently of the distribution to be explained and preserve the same counterfactual pattern at the grain and across the conditions relevant to the question. Applications to wage distributions, cognitive load, and selection clarify this requirement. A hypothetical comparison shows how a lower-level model can preserve the explanation and why matching selected outcome statistics may not suffice. DCD thus describes one form of non-reductive explanation that depends on counterfactual organization rather than additional higher-level productive powers.
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BibTeX
@misc{zhang2026distributional,
title = {{Distributional Counterfactual Dependence in the Special Sciences}},
author = {Youheng Zhang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.34075},
eprint = {2610.34075},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.34075},
}
RIS
TY - UNPB
TI - Distributional Counterfactual Dependence in the Special Sciences
AU - Youheng Zhang
PY - 2026
DA - 2026/10/25
PB - arXiv
DO - 10.48550/arxiv.2610.34075
UR - https://arxiv.org/abs/2610.34075
AB - Special-science explanations often concern distributions rather than individual outcomes. Since higher-level states depend on lower-level processes, what would make a higher-level explanation redundant? I argue that redundancy requires replacement without loss, not merely a reconstruction of individual outcomes. Distributional counterfactual dependence (DCD) identifies how changes in higher-level conditions alter an outcome distribution or a relevant feature of it. Against the background of stochastic supervenience, I distinguish this explanatory role from the dependence of higher-level states on lower-level bases. A replacement must be specified independently of the distribution to be explained and preserve the same counterfactual pattern at the grain and across the conditions relevant to the question. Applications to wage distributions, cognitive load, and selection clarify this requirement. A hypothetical comparison shows how a lower-level model can preserve the explanation and why matching selected outcome statistics may not suffice. DCD thus describes one form of non-reductive explanation that depends on counterfactual organization rather than additional higher-level productive powers.
ER -