{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/130806"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/130806","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning causal graphs under interventions and applications to single-cell biological data analysis","abstract":"This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. 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