The Prospects for a Monist Theory of Non-Causal Explanation in Science and Mathematics

We explore the prospects of a monist account of explanation for both non-causal explanations in science and pure mathematics. Our starting point is the counterfactual theory of explanation (CTE) for explanations in science, as advocated in the recent literature on explanation. We argue that, despite the obvious differences between mathematical and scientific explanation, the CTE can be extended to cover both non-causal explanations in science and mathematical explanations. In particular, a successful application of the CTE to mathematical explanations requires us to rely on counterpossibles. We conclude that the CTE is a promising candidate for a monist account of explanation in both science and mathematics

Commentary: How norms make causes

Kronfeldner M. Commentary: How norms make causes. International Journal of Epidemiology. 2014;43(2):1707-1713.There are always many causes involved in the coming into being of something. If, for instance, there are many factors that are causally involved in the etiology of a disease, even if each only has a small influence, then, in principle, they all have to be taken into account to get a complete causal explanation of the phenomenon at issue. But, as a matter of fact, complete causal explanations are rather hard to get (or too expensive) if not impossible, i.e., completely beyond our scientific abilities. In practice, we usually select even among those causes which are ontologically on a par, e.g., among genetic and environmental factors in the explanation of diseases and give priority to genetic factors. Given this, a philosophical analysis of causal explanations has to account for the partiality and biasedness of causal explanations. Since John Stuart Mill, philosophers have discussed this issue under the label causal selection. The paper presents an approach that revises R. C. Collingwood’s control principle, his answer to the issue, to illustrate how norms make causes. Evidence for the approach stems from the history of cancer research and genetics

Why Is There Universal Macro-Behavior? Renormalization Group Explanation As Non-causal Explanation.

Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal explanations because their explanatory power is due to the application mathematical operations, which do not serve the purpose of representing causal relations

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift\unicode{x2013}\unicode{x2013}a setting that poses a challenge to standard deep learning methods. In this paper, we present Q-SAVI, a probabilistic model able to address these challenges by encoding explicit prior knowledge of the data-generating process into a prior distribution over functions, presenting researchers with a transparent and probabilistically principled way to encode data-driven modeling preferences. Building on a novel, gold-standard bioactivity dataset that facilitates a meaningful comparison of models in an extrapolative regime, we explore different approaches to induce data shift and construct a challenging evaluation setup. We then demonstrate that using Q-SAVI to integrate contextualized prior knowledge of drug-like chemical space into the modeling process affords substantial gains in predictive accuracy and calibration, outperforming a broad range of state-of-the-art self-supervised pre-training and domain adaptation techniques.Comment: Published in the Proceedings of the 40th International Conference on Machine Learning (ICML 2023

On Petition for a Writ of Certiorari to The United States Court of Appeals for The Eighth Circuit, Brief of Law Professors Paul F. Rothstein, et. al., Office of the President v. Office of Independent Counsel

This Court should grant review not only because this is a case of national importance and prominence, but also because the decision below is a conspicuous departure from settled principles of evidence law. The panel majority concluded that communications between government lawyers and government officials are not protected by the attorney-client privilege, at least when those communications are sought by a federal grand jury. That conclusion conflicts with the predominant common-law understanding that the attorney-client privilege applies to government entities and that where the privilege applies, it is absolute (i.e., it protects against disclosure in all types of legal and investigative proceedings). In particular, the Court of Appeals\u27 decision rests on a fundamental misunderstanding of this Court\u27s decisions in Upjohn Co. v. United States, 449 U.S. 383 (1981), and United States v. Nixon, 418 U.s. 683 (1974). Moreover, this case warrants further review because the decision below has profound implications beyond the parties to this dispute. The Court of Appeals\u27 ruling, if allowed to stand, will create widespread uncertainty among federal, state, and local officials concerning the extent to which their communications with their agency lawyers, for the purpose of seeking legal advice in the conduct of governmental affairs, are protected by the attorney-client privilege. Unless this Court grants review and resolves this uncertainty, the decision below will likely have an adverse effect on the current and future operation of not only the Office of the President of the United States, but also government at all levels. At the very least, a decision of such vast implications (as in the present case) should be made by the highest court in the land. We accordingly urge the Court to grant the petition for review

The potential impact of biomarker-guided triage decisions for patients with urinary tract infections

Objectives: Current guidelines provide limited evidence as to which patients with urinary tract infection (UTI) require hospitalisation. We evaluated the currently used triage routine and tested whether a set of criteria including biomarkers like proadrenomedullin (proADM) and urea have the potential to improve triage decisions. Methods: Consecutive adults with UTI presenting to our emergency department (ED) were recruited and followed for 30days. We defined three virtual triage algorithms, which included either guideline-based clinical criteria, optimised admission proADM or urea levels in addition to a set of clinical criteria. We compared actual treatment sites and observed adverse events based on the physician judgment with the proportion of patients assigned to treatment sites according to the three virtual algorithms. Adverse outcome was defined as transfer to the intensive care unit (ICU), death, recurrence of UTI or rehospitalisation for any reason. Results: We recruited 127 patients (age 61.8±20.8 years; 73.2% females) and analysed the data of 123 patients with a final diagnosis of UTI. Of these 123 patients, 27 (22.0%) were treated as outpatients. Virtual triage based only on clinical signs would have treated only 22 (17.9%) patients as outpatients, with higher proportions of outpatients equally in both biomarker groups (29.3%; p=0.02). There were no significant differences in adverse events between outpatients according to the clinical (4.5%), proADM (2.8%) or urea groups (2.8%). The mean length of stay was 6.6days, including 2.2days after reaching medical stability. Conclusions: Adding biomarkers to clinical criteria has the potential to improve risk-based triage without impairing safety. Current rates of admission and length of stay could be shortened in patients with UT

Enhancement of CURB65 score with proadrenomedullin (CURB65-A) for outcome prediction in lower respiratory tract infections: Derivation of a clinical algorithm

Proadrenomedullin (ProADM) confers additional prognostic information to established clinical risk scores in lower respiratory tract infections (LRTI). We aimed to derive a practical algorithm combining the CURB65 score with ProADM-levels in patients with community-acquired pneumonia (CAP) and non-CAP-LRTI