Michaël Bon | Searcharxiv

arXiv2020

Estimating Individual Treatment Effects through Causal Populations Identification

Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning. In this paper, we formulate this problem as an inference from hidden variables and enforce causal constraints based on a model of four exclusive causal populations. We propose a new version of the EM algorithm, coined as Expected-Causality-Maximization (ECM) algorithm and provide hints on its convergence under mild conditions. We compare our algorithm to baseline methods on synthetic and real-world data and discuss its performances.

cs.LG↗

arXiv2017

Novel processes and metrics for a scientific evaluation rooted in the principles of science - Version 1

Scientific evaluation is a determinant of how scientists, institutions and funders behave, and as such is a key element in the making of science. In this article, we propose an alternative to the current norm of evaluating research with journal rank. Following a well-defined notion of scientific value, we introduce qualitative processes that can also be quantified and give rise to meaningful and easy-to-use article-level metrics. In our approach, the goal of a scientist is transformed from convincing an editorial board through a vertical process to convincing peers through an horizontal one. We argue that such an evaluation system naturally provides the incentives and logic needed to constantly promote quality, reproducibility, openness and collaboration in science. The system is legally and technically feasible and can gradually lead to the self-organized reappropriation of the scientific process by the scholarly community and its institutions. We propose an implementation of our evaluation system with the platform "the Self-Journals of Science" (www.sjscience.org).

cs.DL↗