Causal inference in online systems: Methods, pitfalls and best practices

a Workshop

Friday, 4/6/2018, 9:00 am to 5:00 pm.   ARCHIVED EVENT

Location: Weiser Hall, room 747

LSA's Computational Social Science (CSS) Initiative is offering a Winter 2018 series of methods workshops designed to provide introductions to important CSS-related models and techniques, especially for social scientists.

The third of the three CSS Methods Workshops Winter Series is:

CAUSAL INFERENCE IN ONLINE SYSTEMS: METHODS, PITFALLS AND BEST PRACTICES

Taught by Amit Sharma, Microsoft Research, NYC

From recommending what to buy and which movies to watch, to selecting the news to read, the people to follow, and the jobs to apply for, online systems have become an important part of our daily lives. A natural question to ask is how these socio-technical systems impact our behavior. However, because of the intricate interplay between the outputs of these systems and people's actions, identifying their impact on people's behavior is non-trivial. Fortunately, there is a rich body of work on causal inference that we can build on.

In the first part of the tutorial, I will show the value of counterfactual reasoning for studying socio-technical systems, by demonstrating how predictive modeling based on correlations can be counterproductive. Then, we will discuss different approaches to causal inference, including randomized experiments, natural experiments such as instrumental variables and regression discontinuities, and observational methods such as stratification and matching. Throughout, we will try to make connections with graphical models, machine learning and past work in the social sciences.

The second part of the tutorial will be more hands-on. We will work through a practical example of estimating the causal impact of a recommender system, starting from simple to more complex methods. The goal of the practical exercise will be to appreciate the pitfalls in different approaches to causal reasoning and take away best practices for doing causal inference with messy, real-world data.

Friday, April 6, 9am to 5 pm. Weiser Hall, #747.

Lunch provided. Registration required.

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