Approximation Polynomial-Time Algorithms for Minimum Cost Intervention in Causal Effect Identification
Event details
| Date | 29.08.2023 |
| Hour | 14:00 › 16:00 |
| Speaker | Sepehr Elahi |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Michael Gastpar
Thesis advisor: Prof. Patrick Thiran
Thesis co-advisor: Prof. Negar Kiyavash
Co-examiner: Prof. Ola Svensson
Abstract
We investigate two fundamental problems in causality: causal discovery and causal identifiability. For the former, we summarize the important findings of two works, the first of which focuses on learning a causal graph through small-sized interventions, and the second of which looks at the problem of learning a causal graph through single-target noisy interventions. For the latter, we discuss a work that formulated the problem of ensuring identifiability through minimum cost interventions as a combinatorial optimization problem. We also briefly present our own work on the problem of minimum cost intervention for causal effect identification, where we derive bounds on the performance of a polynomial-time algorithm on random graphs.
Background papers
Exam president: Prof. Michael Gastpar
Thesis advisor: Prof. Patrick Thiran
Thesis co-advisor: Prof. Negar Kiyavash
Co-examiner: Prof. Ola Svensson
Abstract
We investigate two fundamental problems in causality: causal discovery and causal identifiability. For the former, we summarize the important findings of two works, the first of which focuses on learning a causal graph through small-sized interventions, and the second of which looks at the problem of learning a causal graph through single-target noisy interventions. For the latter, we discuss a work that formulated the problem of ensuring identifiability through minimum cost interventions as a combinatorial optimization problem. We also briefly present our own work on the problem of minimum cost intervention for causal effect identification, where we derive bounds on the performance of a polynomial-time algorithm on random graphs.
Background papers
Practical information
- General public
- Free