Causality in Biomedicine Lecture Series: Lecture 2 Ava Khamseh - PowerPoint PPT Presentation

Causality in Biomedicine Lecture Series: Lecture 2 Ava Khamseh (Biomedical AI Lab) IGMM & School of Informatics 30 Oct, 2020

<latexit sha1_base64="THysm4IscHt9No/tkD9LNMzeowE=">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</latexit> <latexit sha1_base64="WBS6W4REKXQyW0MTnj/H3EUrYU=">AB/3icdVDLSgMxFM3UV62vUcGNm2AR2k3J1GrHhVB047KCfUBbSiZN29BMZkwyYm78FfcuFDErb/hzr8x01ZQ0QMXTs65l9x7vJAzpRH6sBILi0vLK8nV1Nr6xuaWvb1TVUEkCa2QgAey7mFORO0opnmtB5Kin2P05o3uIj92i2VigXiWg9D2vJxT7AuI1gbqW3vhZm7sT5zsrAp6A2cvVC2badRDqFC8QRBQ05d1y0akj9GBYSgY6wYaTBHuW2/NzsBiXwqNOFYqYaDQt0aYakZ4XSakaKhpgMcI82DBXYp6o1mu4/gYdG6cBuIE0JDafq94kR9pUa+p7p9LHuq9eLP7lNSLdVsjJsJIU0FmH3UjDnUA4zBgh0lKNB8agolkZldI+lhiok1kKRPC16Xwf1LN5yjXP6qkC6dz+NIgn1wADLAUVQApegDCqAgDF4AE/g2bq3Hq0X63XWmrDmM7vgB6y3TxImlNw=</latexit> Last time: Observational data, what goes wrong? p ( x | t = 1) 6 = p ( x | t = 0) Control treatment Age ✓Z ◆ Z Z � � y 1 ( x ) p ( x | t = 1) dx � y 0 ( x ) p ( x | t = 0) dx 6 = y 1 ( x ) � y 0 ( x ) p ( x ) dx

Simpson’s Paradox • Why concluding causality from purely associational measures, i.e. correlation, can be very wrong (not just neutral): “It would have better not to make any statements!” Causal Inference in Statistics, Pearl (2016)

Simpson’s Paradox • Why concluding causality from purely associational measures, i.e. correlation, can be very wrong (not just neutral): “It would have better not to make any statements!” Causal Inference in Statistics, Pearl (2016)

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Overview of the course • Estimating causal effects • Randomised trial vs observational data Causal Inference Causal Effect Estimation Casual Discovery Obsv confounders Unobsv confounders Constraint- Score- FCM based based Front- Propensity Regression IV door score Adjustment Modern ML criterion Rubin Rubin, Pearl

Causal inference with observed confounders X T Y

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