Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019

Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019
Share:

Download MP3


Similar Tracks

An introduction to Causal Inference with Python – making accurate estimates of cause and effect from PyCon AU
Kevin Klein - Causal Inference Libraries: What They Do, What I'd Like Them To Do | PD Amsterdam 2023 PyData
Jessica Tyler: Geo Experiments and Causal Impact in Incrementality Testing | PyData New York 2019 PyData
Diego Torres Quintanilla: Cleaning, optimizing and windowing pandas with numba | PyData NYC 2019 PyData
Propensity Modeling to Identify Behaviors and Attributes of High-Value Customers InfoTrust
Propensity score weighting Philip Moons
Propensity Score Analysis in R with Nearest Neighbor, Optimal Pair, and Optimal Full Matching statsguidetree
Nick Jones, Sam Barrows: Uber's Synthetic Control | PyData Amsterdam 2019 PyData
What is causal inference, and why should data scientists know? by Ludvig Hult PyCon Sweden
An intuitive introduction to Propensity Score Matching Doug McKee
Gary King, "Why Propensity Scores Should Not Be Used for Matching" Methods Colloquium
Customer Lifetime Value: Models, Metrics and a Multitude of Uses - Brian Bloniarz PyData
Introduction to Causal Inference: Propensity Score Analysis in Healthcare Data Population Data BC
Lecture 11 Propensity Score Matching Richard Gallenstein
16. Matching Methods Gary King
Causal Effects via Propensity Scores | Introduction & Python Code Shaw Talebi
Causal Inference made easy with Inverse Propensity Weighting /( Gerben Oostra, PyData TLV Oct 21) PyData
Causal Inference - EXPLAINED! CodeEmporium
14. Causal Inference, Part 1 MIT OpenCourseWare
Pedro Tabacof - Unlocking the Power of Gradient-Boosted Trees (using LightGBM) | PyData London 2022 PyData