Causal Inference in Python: Theory to Practice

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  • Опубликовано: 22 ноя 2024

Комментарии • 10

  • @sroy2138
    @sroy2138 10 месяцев назад +9

    This is a highly informative and useful presentation. It is clear, concise, and to the point.

  • @amirahheng3454
    @amirahheng3454 Месяц назад +3

    00:20 Introduction to causal inference theory and practice
    02:35 Common causes can explain apparent correlations.
    06:58 Traditional data science pipeline vs causal pipeline
    09:08 Represent problem as a causal DAG
    13:30 Understanding causal effects through interventions and surgeries on the DAG arrows
    15:41 Identification and back door path
    19:44 Causal methods resolve paradoxes in identifying effects
    21:36 Causal inference methods for regression analysis
    25:25 Using a causal pipeline to analyze the effect of subscribing on spending.
    27:12 Representing causal diagrams and data frame creation for causal modeling
    30:45 Adjust variables to run estimation and implement estimation methods.
    32:40 Control only for variables in the minimal set for computational efficiency.
    36:29 Testing consistency of DAG with data using conditional independencies
    38:31 Testing independence between X and Y based on partial correlation coefficient
    42:22 Identifying minimal sufficient adjustment sets for estimating total effects
    Crafted by Merlin AI.

  • @gunbac74
    @gunbac74 3 месяца назад +1

    How can I download the Jupyter Notebook presented in the video?

  • @bodwiser100
    @bodwiser100 Месяц назад

    On slide 24, you mentioned that conditional on Z, if there is a significant dependence between X and Y, then the DAG is possibly wrong. I am confused why? Why could it not mean that there is actually a legit causal relationship between X and Y ?

  • @anveshikakamble3717
    @anveshikakamble3717 9 месяцев назад +2

    Without the data, I am unable to see any estimands. For all the 3 estimands it shows no such variables found. How can I know what variables to adjust ?

    • @ЮрийИващенко-щ7я
      @ЮрийИващенко-щ7я 6 месяцев назад +1

      Good challenge - you can try to create synthetic data (column names provided) based on your assumptions for distributions/rules and see what will happen ;)

  • @furxia
    @furxia Месяц назад

    Where can we download the full_data.csv?

    • @DataScienceFestival
      @DataScienceFestival  Месяц назад

      Hey! You can access all relevant resources as referred to in this talk via our website: datasciencefestival.com/session/causal-inference-in-python-theory-to-practice/

  • @NugrohoBudianggoro
    @NugrohoBudianggoro 8 месяцев назад +1

    bookmarking 23:08