Causal Data Science: Black Boxes, Black Swans, Black Markets and Black Holes!


Join us to explore causality in data science with speakers Jed Dougherty of Dataiku and renowned NYU Professor Bud Mishra!

Many thanks to NYC Data Science Academy ( for hosting us.

A recent analysis, conducted by the Federal Reserve Board, showed that before and during the financial crisis of 2008 the average bank profit-and-loss (PnL) did not exceed the bank VaR from December 2003 to April 2007, while the bank average PnL exceeded VaR six times from June 2007 to March 2008. In other words, banks which maintained capital reserves equal to their Value-at-Risk faced on average six near bankruptcies during the crisis. What most intrigued the economists and political and social scientists, was the sheer lack of a single plausible causal explanation of these events – which is thought to have ranged from (i) “One eyed Scottish idiot!” (Jeremy Clarkson); (ii) “Complex financial products; undisclosed conflicts of interest; the failure of regulators, the credit rating agencies, and the market itself,” Interest Rate Spreads, Emerging Markets: e.g., BRICS – and so on. Both the unusual abruptness and intuitive implausibility earned such scenarios the name, “Black Swan Events” – and many more. It also raised the question whether there is a theory of “causality” that could have rigorously explained such events empirically from data – we suggest that the machinery of model checking for a suitably expressive logic (e.g., PCTL Probabilistic Computational Tree Logic, a branching time propositional modal logic) provides just the right capabilities to succinctly specify and efficiently verify statements about such scenario. It derives its power from the way it combines logic, probability and reasoning about time, which are lacked in the currently popular black-box AI systems.

About Professor Mishra:
NYU Courant Institute Professor Bhubaneswar "Bud" Mishra is a mentor, a teacher and a thinker, helping students, entrepreneurs and collaborators, solving problems in statistics, machine learning, and data science with applications to AdTech, BioTech, FinTech, InfoTech, RegTech, etc. He was recently named a Fellow of the National Academy of Inventors (NAI) for his seminal work in these technologies. Mishra holds 21 issued and 23 pending patents in areas ranging over robotics, model checking, intrusion detection, cyber security, emergency response, disaster management, data analysis, biotechnology, nanotechnology, genome mapping and sequencing, mutation calling, cancer biology, financial technology, advertising technology, Internet architecture, and linguistics. He has industrial experience in computer and data science, finance, robotics and bio- and nanotechnologies, and is the author of a textbook on algorithmic algebra and more than 200 archived publications.

About Jed Dougherty:
Jed leads Dataiku's Data Science team in North America. He works with a wide variety of Fortune 500 clients and specializes in helping large companies spin up and organize Data Science teams. Before coming to Dataiku he worked on event detection, spam filtering, and survival analysis in the fields of breaking news, social media, and child welfare. He earned his masters at Columbia University in its QMSS program.

6:00 PM: Pizza, beer, & networking
6:30 PM: Talk by Jed Dougherty, Lead Data Scientist at Dataiku
7:00 PM: Talk by Professor Bud Mishra, NYU Courant Institute of Mathematical Sciences

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