אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Causal inference is fundamental to empirical research in natural and social sciences and is essential for scientific discoveries. Two key challenges for conducting causal inference are (i) acquiring all attributes required for the analysis, and (ii) identifying which attributes should be included in the analysis. Failing to include all necessary attributes may lead to false discoveries and erroneous conclusions. However, in real-world settings, analysts may only have access to partial data. Further, to identify which attributes should be included in the analysis, analysts critically rely on domain knowledge, often given in the form of a causal DAG. However, such domain knowledge is often unavailable and cannot be fully recovered from data. In this talk we will present two works that address these challenges by leveraging data management techniques and ideas. Bio: Brit is a postdoc researcher at CSAIL MIT, working with Prof. Michael Cafarella. She received her Ph.D. at Tel-Aviv University under the supervision of Prof. Tova Milo. Her research is centered around informative and responsible data science and causal analysis. Brit is the recipient of several awards, including the data science fellowship for outstanding Ph.D. students of the planning and budgeting committee of the Israeli council for higher education (VATAT), the Schmidt postdoctoral award for women in mathematical and computing sciences, and the planning and budgeting committee of the Israeli council for higher education (VATAT) postdoctoral scholarship in Data Science. She served on multiple program committees, including at the SIGMOD and ICDE conferences.