Skip to content
Carmelics
TopicsThinkersChangesContributorsLoading account…

    Carmelics

    A reasoning platform. Break down any belief into clear reasons, explore both sides, and weigh the evidence honestly.

    Navigate

    • Topics
    • Search
    • Recent Changes
    • Contribute
    • How It Works
    • Glossary
    • Thinkers
    • Contributors
    • About
    • Statistics
    • Terms
    • Privacy

    Database

    Statements
    —
    Perspectives
    —
    Topics
    —

    Press ? for keyboard shortcuts

    LoyalLoyalJusticeJustice
    Made withinDC&Austin
    Statements
    321,452
    Perspectives
    108,905
    Topics
    42
    Home/Original/inverse
    See Original
    Inverse View

    It is not the case that If a variable set is causally sufficient, then the error variables for any two variables in that set are probabilistically independent.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
    ?
    • 1.Causal sufficiency requires no unmeasured common causes, but latent variables can induce error correlations without being 'causes' in the system.
      ?

      Think about whether this reason is strong or weak

    • 2.Spirtes, Glymour & Scheines acknowledge that cyclic causal structures can produce correlated errors even in causally sufficient sets.
      ?

      Think about whether this reason is strong or weak

    • 3.Therefore, causal sufficiency as standardly defined does not guarantee probabilistic independence of error terms in non-acyclic models.
      ?

      Think about whether this reason is strong or weak

    Reason for 2 of 2
    ?
    • 1.Cartwright argues that the Common Cause Principle fails in quantum entanglement cases, where correlated variables share no screener-off common cause.
      ?

      Think about whether this reason is strong or weak

    • 2.If the CCP admits physically realized exceptions, probabilistic independence cannot be derived from mere absence of causal relations between error terms.
      ?

      Think about whether this reason is strong or weak

    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Causal sufficiency of the variable set implies that error variables U_X and U_Y are causally unrelated.
      ?

      Think about whether this reason is strong or weak

    • 2.The Common Cause Principle holds that variables without a common cause or direct causal relationship are probabilistically independent.
      ?

      Think about whether this reason is strong or weak

    Next step

    Based on where you are in your exploration

    Strongest counterpoint
    Explore the most compelling reason on the other side.