Books, and papers

Full Law Identification In Graphical Models Of Missing Data: Completeness Results

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence...

CAUSAL RELATIONAL LEARNING

Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard...

Artificial Intelligence with Python: Your Complete Guide to Building Intelligent Apps Using Python 3.x and TensorFlow 2

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated...

The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About

Daniel K. Griffin Staff Data Scientist [email protected] Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering ‘The...

Using Causal Reasoning To Guide Algorithms Toward a Fairer World

ILYA SHPITSER John C. Malone Assistant Professor of Computer Science, Johns Hopkins University DANIEL MALINSKY Researcher, Johns Hopkins University   Learning algorithms, which are becoming an increasingly ubiquitous part of our lives, do precisely...

From how to why: An overview of causal inference in machine learning

Ravi Pandya Artificial intelligence is good at predicting outcomes, but how do we go one step further? Here, we discuss how AI can use causal inference and machine learning to measure the effects of multiple variables – and why it’s important for...