Causal Inference -- 6/23 -- Local Average Treatment Effect II

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  • เผยแพร่เมื่อ 1 ม.ค. 2025

ความคิดเห็น • 13

  • @c.comploj3775
    @c.comploj3775 ปีที่แล้ว +1

    This is the best verbal explanation of LATE anywhere after 6 years of study.

  • @akileshbadrinaaraayanan5989
    @akileshbadrinaaraayanan5989 3 ปีที่แล้ว +1

    Thanks for the videos, could you explain how the other probabilities are computed for complier and always-taker at 34:39? More specifically, my question is shouldn't 0.3112 (1-Pr(never-taker)) = Pr(always-taker) + Pr(complier), leading to Pr(always-taker) = 0.187 and Pr(complier) = 0.1242?

    • @ben_elsner
      @ben_elsner  3 ปีที่แล้ว +1

      Hi Akilesh, thanks a lot for spotting this. You are right. I'll change that soon. Regards,
      Ben

    • @jayagupta9239
      @jayagupta9239 2 ปีที่แล้ว

      @@ben_elsner hii, can you please explain how to compute the probability of complier

    • @ben_elsner
      @ben_elsner  2 ปีที่แล้ว

      Please check the post of Akilesh further above. His probabilities are correct.

    • @ben_elsner
      @ben_elsner  2 ปีที่แล้ว

      @@jayagupta9239 Check the relevant slide. There are four equations and four unknowns. From this system of equations you can back out the share of compliers.

  • @annawilson3824
    @annawilson3824 11 หลายเดือนก่อน

    33:13

  • @LoiseMusic
    @LoiseMusic 3 ปีที่แล้ว

    GOAT

  • @NoName-kg5rv
    @NoName-kg5rv 2 ปีที่แล้ว +1

    I love you after watching this set of videos. If I were a woman, I would want to marry you.

    • @ben_elsner
      @ben_elsner  2 ปีที่แล้ว +1

      I can give you my dad’s number, perhaps you could talk about the dowry.

  • @specfunctor
    @specfunctor 3 ปีที่แล้ว

    Perhaps a typo at 17:14 : The second term in the last line should be E[Y_i0 | never-taker].
    These are great lectures, thanks a lot!

    • @ben_elsner
      @ben_elsner  3 ปีที่แล้ว +1

      I believe you're correct. Thanks for pointing this out. Will correct next week.