9 papers
Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory
Hanti Lin
This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical premise. Although related chal…
Meager Success: A Theory of the Unlearnable for Hypothesis Testing
Hanti Lin
When the standard of pointwise consistency for statistical inference -- convergence to the truth in every possible state of the world -- is provably unachievable, the usual respons…
Never Too LATE: A Fully Stochastic Update to the Potential Outcome Framework
Hanti Lin
In the classic potential outcome framework, the local average treatment effect (LATE) and its identification via an instrumental variable are stated in a deterministic setting at t…
A Plea for History and Philosophy of Statistics and Machine Learning
Hanti Lin
The integration of the history and philosophy of statistics was initiated at least by Hacking (1975) and advanced by Hacking (1990), Mayo (1996), and Zabell (2005), but it has not…
The Problem of the Priors, or Posteriors?
Hanti Lin
The problem of the priors is well known: it concerns the challenge of identifying norms that govern one's prior credences. I argue that a key to addressing this problem lies in con…
The Logic of Counterfactuals and the Epistemology of Causal Inference
Hanti Lin
The 2021 Nobel Prize in Economics recognized an epistemology of causal inference based on the Rubin causal model (Rubin 1974), which merits broader attention in philosophy. This mo…