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Amit Sharma

Postdoctoral Researcher
Microsoft Research, New York

Blog: A gentle introduction to causal inference
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Research Summary

My work spans a broad range of research areas, connected through a central goal of understanding human behavior through online systems. Technological advances in systems and sensors have provided an opportunity to observe what is happening (e.g., purchases, clicks) in minute detail, but it is much harder to say why. My research aims to bridge this gap between the whats and whys, employing techniques from machine learning, statistics and experimentation to discover causal patterns that lead to the activity and decisions we see.

For example, when we go shopping, how do the website's recommendations affect our final purchase? When we choose what to read, how much do our friends (or celebrities) influence our behavior? While we have been able to map out precise trajectories to far off planets like Pluto, earthly problems like these have escaped convincing answers.

At Microsoft Research, I am currently working on building methods for tackling problems like these. I received my Ph.D. from Cornell, advised by Dan Cosley, where I primarily studied social recommender systems. Prior to graduate school, I studied computer science at IIT Kharagpur.


Data mining for Instrumental Variables: Estimating the causal impact of recommender systemsEC 2015 | Paper

Preference Model-based Matching: Distinguishing between personal preference and social influence in social activity feedsCSCW 2016 Hon'ble Mention | Paper

Simpson's Paradox in Reddit: Using Time-aware analyses to better understand behaviorWWW 2016 | Paper


June 2016

Tutorial on causal inference at the 2nd International Computational Social Science (IC2S2) conference. Slides and code on Github!

April 2016

Invited talk at MIT Marketing Seminar on data mining for causal inference and its applications in estimating impact of recommender systems. [Slides]

Feb 2016

Honorable mention for Best paper award at the 19th ACM CSCW Conference for our paper on estimating social influence through social activity news feeds.

Selected Talks

Work Experience

Microsoft Research

New York, USA
July 2016 - Current

Postdoctoral Researcher in Computational Social Science.

Microsoft Research

New York, USA
May - August 2014

Worked with Jake Hofman and Duncan Watts.
Estimation of causal impact of recommender systems.

Google Inc.

Mountain View, USA
May - August 2013

Worked with Gueorgi Kossinets.
Inference of attributes for local businesses, studying the evolution of their rating.

LinkedIn Corp.

Mountain View, USA
May - August 2012

Worked with Baoshi Yan.
Novel pairwise models for learning implicit user feedback on recommendations.


Lausanne, Switzerland
May - July 2009

Worked with Frederic Kaplan and Pierre Dillenbourg.
Prosodic analysis of speech and visualizations for supporting collaborative dialogue.

IBM Research

New Delhi, India
May - July 2008

Worked with Akshat Verma and Gargi Dasgupta.
Local-optimal search algorithms for dynamic composition of web services.


Honors and Awards

Working papers

Conference Papers

Journal Papers

Workshop and Demos

Datasets for social recommendation and diffusion

When I started working on recommendations within social networks, there were not many datasets available that had both social connections and people's preferences. Even the ones that existed were hard to find, so I am listing out relevant datasets that are useful for studying people's preferences in social networks and designing social recommender systems.

Contact Me