Stanford Root

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Stanford Root

Schedule

DATASCI 161

Causality, Decision Making and Data Science (CS 171, ECON 115)

UNITS:3
GRADING:Letter or Credit/No Credit
LEVEL:Undergrad
GER:—

Policymakers often need to make decisions when the implications of those decisions are not known with certainty. In many cases they rely in part on statistical evidence to guide these decisions. This requires statistical methods for estimating causal effects, that is the impact of these interventions. In this course we study how to analyze causal questions using statistical methods. We look at several causal questions in detail. For each case, we study various statistical and econometric methods that may shed light on these questions. We discuss what the critical assumptions are that underly these methods and how to assess whether the methods are appropriate for the settings at hand. We then analyze data sets, partly in class, and partly in assignments, to see how much we learn in practice. Pre-requisites: One quarter course in statistics, at the level of STATS DATASCI 116 or STATS DATASCI 117. Programming experience with Python will be helpful but is not required. Approved field credit for the ECON BA, BS, and Minor Programs.

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DATASCI 161: Causality, Decision Making and Data Science (CS 171, ECON 115)

3 units · Letter or Credit/No Credit

Policymakers often need to make decisions when the implications of those decisions are not known with certainty. In many cases they rely in part on statistical evidence to guide these decisions. This requires statistical methods for estimating causal effects, that is the impact of these interventions. In this course we study how to analyze causal questions using statistical methods. We look at several causal questions in detail. For each case, we study various statistical and econometric methods that may shed light on these questions. We discuss what the critical assumptions are that underly these methods and how to assess whether the methods are appropriate for the settings at hand. We then analyze data sets, partly in class, and partly in assignments, to see how much we learn in practice. Pre-requisites: One quarter course in statistics, at the level of STATS 116 or STATS 117. Programming experience with Python will be helpful but is not required. Approved field credit for the ECON BA, BS, and Minor Programs.

More DATASCI courses

  • DATASCI 112: Principles of Data Science
  • DATASCI 120: Data Narratives
  • DATASCI 154: Data Science for Social Impact (COMM 140X, EARTHSYS 153, ECON 163, MS&E 134, POLISCI 154, PUBLPOL 155, SOC 127)
  • DATASCI 156: Thinking and Making with Data (ENGLISH 156A)
  • DATASCI 190: The Data Science Experience
  • DATASCI 192A: Data Science Practicum I
  • DATASCI 192B: Data Science Practicum II
  • DATASCI 193: Applied Artistic and Cultural Analysis
  • DATASCI 194B: Data Science for Computational Molecular Biology (DATASCI 294B)
  • DATASCI 194C: Driving Innovation: Benchmarks, Competitions, and Challenge Problems in Machine Learning and Beyond (DATASCI 294C)
  • DATASCI 194L: Data Science and the Science of Learning (DATASCI 294L, EDUC 139, PSYCH 139)
  • DATASCI 194N: Data Science for Neuroscience (DATASCI 294N, PSYCH 294N)

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