Inverse Conditional Probability Weighting with Clustered Data in Causal Inference

As is well known, presidential election outcomes are somewhat predictable based on economic performance. Votes for the U.S. Congress, are to a large part determined by party balancing. Right now, the Republicans control the executive branch, both houses of congress, and the judiciary, so it makes sense that voters are going to swing toward the Democrats. Political scientists Bob Erikson, Joe Bafumi, and Chris Wlezien have written a lot about this; see for example here, here, and here.

Apache Calcite Apache Calcite is a Dynamic Data Management Framework. It Contains Many of the Pieces That Comprise a Typical Database Management System, but Omits Some key Functions: Storage of Data, Algorithms to Process Data, and a Repository for Storing Metadata. Calcite Intentionally Stays out of the Business of Storing and Processing Data. As we Shall See, This Makes it an Excellent Choice for Mediating Between Applications and one or More Data Storage Locations and Data Processing Engines. It is Also a Perfect Foundation for Building a Database: Just add Data. Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources …

Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must be evolutionary continuous phenomena. How and why did emotions evolve in nature, how do events get emotionally appraised, how do emotions relate to cognitive complexity, and, how do they impact behavior and learning? In this article I propose that all emotions are manifestations of reward processing, in particular Temporal Difference (TD) error assessment. Reinforcement Learning (RL) is a powerful computational model for the learning of goal oriented tasks by exploration and feedback. Evidence indicates that RL-like processes exist in many animal species. Key in the processing of feedback in RL is the notion of TD error, the assessment of how much better or worse a situation just became, compared to what was previously expected (or, the estimated gain or loss of utility – or well-being – resulting from new evidence). I propose a TDRL Theory of Emotion and discuss its ramifications for our understanding of emotions in humans, animals and machines, and present psychological, neurobiological and computational evidence in its support. A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior

Today, being data driven means doing machine learning (ML) and artificial intelligence (AI) at a more macro level. But it also means empowering *all *employees at all levels of the company to use data in innovative ways for faster and better decisions in their day-to-day work. Unfortunately, businesses still operating with data silos will simply spin their wheels on both initiatives.

From xkcd, a blockbuster idea right here.

What is Granger Causality?

Declare and Diagnose Research Designs (DeclareDesign)Researchers can characterize and learn about the properties of research designs before implementation using DeclareDesign. Ex ante declaration and di …

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Generation Meets Recommendation: Proposing Novel Items for Groups of Users