Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.Comment: Accepted to Transactions of the Association for Computational Linguistics (TACL

Diffusion-limited aggregation: A relationship between surface thermodynamics and crystal morphology

We have combined the original diffusion-limited aggregation model introduced by Witten and Sander with the surface thermodynamics of the growing solid aggregate. The theory is based on the consideration of the surface chemical potential as a thermodynamic function of the temperature and nearest-neighbor configuration. The Monte Carlo simulations on a two-dimensional square lattice produce the broad range of shapes such as fractal dendritic structures, densely branching patterns, and compact aggregates. The morphology diagram illustrating the relationship between the model parameters and cluster geometry is presented and discussed.Comment: 5 pages, 6 figure

Ashkenazi Jewish Centenarians Do Not Demonstrate Enrichment in Mitochondrial Haplogroup J

BACKGROUND: Association of mitochondrial haplogroup J with longevity has been reported in several population subgroups. While studies from northern Italy and Finland, have described a higher frequency of haplogroup J among centenarians in comparison to non-centenarian, several other studies could not replicate these results and suggested various explanations for the discrepancy. METHODOLOGY/PRINCIPAL FINDINGS: We have evaluated haplogroup frequencies among Ashkenazi Jewish centenarians using two different sets of matched controls. No difference was observed in the haplogroup J frequencies between the centenarians or either matched control group, despite adequate statistical power to detect such a difference. Furthermore, the lack of association was robust to population substructure in the Ashkenazi Jewish population. Given this discrepancy with the previous reported associations in the northern Italian and the Finnish populations, we conducted re-analysis of these previously published data, which supported one of several possible explanations: i) inadequate matching of cases and controls; ii) inadequate adjustment for multiple comparison testing; iii) cryptic population stratification. CONCLUSIONS/SIGNIFICANCE: There does not exist a universal association of mitochondrial haplogroup J with longevity across all population groups. Reported associations in specialized populations may reflect genetic or other interactions specific to those populations or else cryptic confounding influences, such as inadequate matching attributable to population substructure, which are of general relevance to all studies of the possible association of mitochondrial DNA haplogroups with common complex phenotypes

Custom Integrated Circuits

Contains reports on twelve research projects.Analog Devices, Inc.International Business Machines, Inc.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001)U.S. Air Force - Office of Scientific Research (Grant AFOSR 86-0164)Rockwell International CorporationOKI Semiconductor, Inc.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Charles Stark Draper LaboratoryNational Science Foundation (Grant MIP 84-07285)National Science Foundation (Grant MIP 87-14969)Battelle LaboratoriesNational Science Foundation (Grant MIP 88-14612)DuPont CorporationDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research (Contract N00014-87-K-0825)American Telephone and TelegraphDigital Equipment CorporationNational Science Foundation (Grant MIP-88-58764

Autonomous Underwater Vehicle Navigation

This paper surveys the problem of navigation for autonomous underwater vehicles (AUVs). Marine robotics technology has undergone a phase of dramatic increase in capability in recent years. Navigation is one of the key challenges that limits our capability to use AUVs to address problems of critical importance to society. Good navigation information is essential for safe operation and recovery of an AUV. For the data gathered by an AUV to be of value, the location from which the data has been acquired must be accurately known. The three primary methods for navigation of AUVs are (1) dead-reckoning and inertial navigation systems, (2) acoustic navigation, and (3) geophysical navigation techniques. The current state-of-the-art in each of these areas is summarized, and topics for future research are suggested.