Causal Inference - EXPLAINED!

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  • Опубликовано: 11 окт 2024
  • Follow me on M E D I U M: towardsdatasci...
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    REFERENCES
    [1] MIT lecture on Causal Inference. Great for the basic idea and big picture: • 14. Causal Inference, ...
    [2] Great 3 part blogpost that delves into more detail by Microsoft: / causal-inference-part-...
    [3]: More about X-learner and how it overcomes T-learner (high variance) and S-learners (high bias): • 6.3 - TARNet and X-Lea...
    [4] Good Discussion on when Partial Dependency Plots can be used to infer causality: web.stanford.e...
    [5] Blog based on 2: lmc2179.github...
    [6]: CMU blog post on causality: blog.ml.cmu.ed...
    [7] Microsoft’s blog on causal inference: / causal-inference-part-...
    [8] Advanced Discussion: www.inference....
    [9] 3 layers of the causal hierarchy: web.cs.ucla.edu...

Комментарии • 81

  • @CodeEmporium
    @CodeEmporium  2 года назад +108

    I put a lot of effort into this one to make it as descriptive as possible. It's also a new style of delivering content / animation. Please let me know how you like this. :)

  • @andrews9719
    @andrews9719 2 года назад +12

    I’m taking a masters in data analytics/program evaluation, and am learning this rn. You summarize the information really well, picking out the really important parts of causal inference to explain. Good job! The later part of the video even helped me conceptualize quasi experimental designs, which use matching like you described. Thanks for the help.

  • @kinanradaideh5479
    @kinanradaideh5479 Год назад +5

    Absolutely beautiful, incredible explanation; I like that it's explained through a practical example!
    You're very underrated; the future of this channel is bright!

  • @LNJP13579
    @LNJP13579 4 месяца назад +1

    Brother, you have summarized really well in such a short video. Every second was GOLD 🙂

  • @poniard123
    @poniard123 2 года назад +2

    Very useful video. I spent two days reading the actual paper of causal influence. This video is concise but gives me a very good foundation to read the theory.

    • @CodeEmporium
      @CodeEmporium  2 года назад +1

      That’s the hope! Thanks a ton for watching

  • @narkprix2
    @narkprix2 Год назад +1

    Incredibly well explained and very illustrative examples. Many thanks for the work you put on it.

  • @rajns8643
    @rajns8643 7 месяцев назад

    Absolutely beautiful and incredible explanation. I also like the fact that it's explained through a practical example :)
    Cheers!!

  • @gemini_537
    @gemini_537 6 месяцев назад

    Gemini: The video is about causal inference. It explains what causal inference is and the challenges of performing causal inference using observed data. It also explains different techniques to address these challenges.
    The video starts with explaining randomized controlled trials (RCTs) which is the gold standard for causal inference. But RCTs are not always possible. So the video talks about causal inference using observed data.
    Causal inference using observed data is challenging because there can be confounding variables that affect both the treatment and the outcome. The video uses an example of a medical trial for the flu cure to illustrate this point. In the example, age is a confounding variable. The treatment group (people who received the elixir) has an average age of 35 while the control group (people who did not receive the elixir) has an average age of 65. Even if the people in the treatment group recover from the flu faster than the people in the control group, it might be because they are younger, not because of the elixir.
    Another challenge of causal inference using observed data is selection bias. Selection bias happens when the group chosen for the treatment is not representative of the population. For example, if the people who received the elixir in the medical trial were all young and healthy people, then the results of the trial would not be generalizable to the whole population.
    The video also talks about counterfactuals, which are what would have happened if a person had not received the treatment. Counterfactuals are necessary to estimate the causal effect of the treatment. There are two techniques for estimating counterfactuals: matching and machine learning.
    Matching involves finding people in the control group who are similar to the people in the treatment group on all observable characteristics except for the treatment. The outcome of the people in the control group can then be used as an estimate of the counterfactual for the people in the treatment group.
    Machine learning can also be used to estimate counterfactuals. A machine learning model can be trained on data from people who did not receive the treatment. The model can then be used to predict what would have happened to the people in the treatment group if they had not received the treatment.
    The video then talks about the assumptions that need to be made for causal inference using observed data. These assumptions are necessary to make the analysis possible. One of the assumptions is called the causal Markov condition. This assumption says that the treatment only affects the outcome through the variables that are included in the causal graph.
    Another assumption is called SUTVA (Stable Unit-Treatment Value Assumption). This assumption says that the outcome of a unit would be the same no matter what treatment the other units receive.
    The last assumption is called ignorability. This assumption says that there are no confounding variables that have not been included in the analysis.
    The video then shows how to calculate the average treatment effect (ATE) and the conditional average treatment effect (CATE). The ATE is the average difference in the outcome between the treatment group and the control group. The CATE is the average treatment effect for a specific subgroup of the population.
    In the example of the medical trial, the ATE was 0.1. This means that the people who received the elixir were more likely to recover from the flu than the people who did not receive the elixir. However, the CATE for people over the age of 35 was 0.4, while the CATE for people under the age of 35 was -0.2. This means that the elixir was effective for older people but not for younger people.
    The video concludes by saying that causal inference using observed data can be a powerful tool for making decisions, but it is important to be aware of the challenges and assumptions involved.

  • @chrislin5938
    @chrislin5938 2 года назад +2

    This content is gold. Thank you so much for making these kinds of videos!! Can’t wait to see more!!

  • @won20529jun
    @won20529jun 2 года назад +2

    Amazing explanation! It must've been almost painful to not discuss all the details and caveats and technicalities, but that's what made it valuable for me
    Love the music as well :D

  • @andrewchen7710
    @andrewchen7710 Год назад

    reading a paper utilizing causal inference rn - this cleared so much up, props!

  • @tnmyk_
    @tnmyk_ 2 месяца назад

    Amazing explanation! Got to learn a lot and understood everything. Thanks a lot!

  • @mark2292
    @mark2292 Год назад

    Thank you so much, your explanation is way more clear than my prof in this class

    • @CodeEmporium
      @CodeEmporium  Год назад

      Super glad you feel this way! Thanks so much for watching!

  • @franklynLP
    @franklynLP 2 года назад

    My prof needed 3h to explain this (and failed - thats why im here). Thanks for the video, helped a lot!

  • @InCaseofEconStruggles
    @InCaseofEconStruggles Год назад

    I think this is a really good overview of Causal Inference and the main assumptions! Good DAG explanation as well!

    • @CodeEmporium
      @CodeEmporium  Год назад +1

      Thanks so much for watching ! And the comment

  • @clarezhou1652
    @clarezhou1652 Год назад

    Great video. I read a lot of materials and couldn't digest. This one is the best I saw.Thanks

    • @CodeEmporium
      @CodeEmporium  Год назад

      Glad it was helpful! And thanks for watching!

  • @ravisawhney8677
    @ravisawhney8677 10 месяцев назад

    Really good video - appreciate the effort that it must have taken to convey the concepts intuitively whilst being as succinct as possible. Not easy!

  • @tomoki-v6o
    @tomoki-v6o 2 года назад +1

    I think the job you doing in this videos is better than some research papers ,by simplifying this topics for the public
    .

  • @kunaldang7611
    @kunaldang7611 2 года назад +1

    Thanks a lot for this video! Keep up the good work, and please try to cover Causal Graphs (Directed Acyclic Graphs) vs Bayesian Network structure learning(also in detail) if you can. Thanks in advance.

  • @tiff-anniekenny1222
    @tiff-anniekenny1222 2 года назад

    Thank you for this exceptionally well-presented video - rich in content and succinct.

  • @AndyHo-j3f
    @AndyHo-j3f 7 месяцев назад

    This is a really really really well done video, thank you!

  • @zbigniewz.lewandowski8629
    @zbigniewz.lewandowski8629 2 месяца назад

    All of calculations are simple and clear but there is lack of a key element, which you mention at 11:31, namely how to estimate missing data. Could you send a link to an explanation of this element of the presentation?

  • @spencerantoniomarlen-starr3069

    Could you please upload a separate dedicated tutorial teaching us how to say "efficacy" the way you do!

    • @CodeEmporium
      @CodeEmporium  Год назад

      Haha. I shall put that in my todos :)

  • @hameddadgour
    @hameddadgour 6 месяцев назад

    Great video. Thank you for sharing!

  • @mahrym984
    @mahrym984 3 месяца назад

    Great summary thank you🙏🏾

  • @victorrodriguez5981
    @victorrodriguez5981 2 года назад +1

    great explanation, i've been studying c.i. for the past 6 months and your way of explaining was very clear.
    Cheers from Bolivia.
    P.S. can you share your discord link again plz

    • @CodeEmporium
      @CodeEmporium  2 года назад

      Thank you! Appreciated! The discord link should be on the description of this video :)

  • @syhusada1130
    @syhusada1130 2 года назад

    Clear and good explanation.

  • @user-wr4yl7tx3w
    @user-wr4yl7tx3w Год назад

    Well explained.

  • @sanjaykrish8719
    @sanjaykrish8719 7 месяцев назад

    Great video. small correction, pronunciation is causal and not caushal

  • @MSalman1
    @MSalman1 2 года назад

    Awesome job explaining!!!!

  • @zbigniewz.lewandowski8629
    @zbigniewz.lewandowski8629 2 месяца назад

    Your presentation is missing a key element, which you mention at 11:31, namely how to estimate missing data. Could you send a link to an explanation of this element of the presentation?

  • @patpattamon
    @patpattamon 7 месяцев назад

    Thank you so much.

  • @pushkarparanjpe
    @pushkarparanjpe Год назад

    Thanks!

  • @masteronepiece6559
    @masteronepiece6559 2 года назад +2

    Great video. 👏

  • @user-wr4yl7tx3w
    @user-wr4yl7tx3w Год назад

    An idea. Can you consider a video comparing this to Bayesian network.

  • @taotaotan5671
    @taotaotan5671 2 года назад

    Does including the confounder variable in a multiple regression also “control” for false association.

    • @leongyinyee2182
      @leongyinyee2182 2 года назад

      Yeah, I have the same question. But the matching approach seems very usual in biostatistics. I am still figuring their difference.

  • @alghanimaa
    @alghanimaa Год назад

    hello, what is the problem with the following approach which aims to account for age without counterfactuals?
    you can do mean(treatment) - mean(control) for the older group ((0+1+1)/3 = .67) - ((1+0)/2 = .5) resulting in a difference of .17 for the older group and a similar calculation for the younger group yields ((1+0)/2 = .5) - ((1+0+0)/3 = .33) resulting in a difference of .17 for the younger group as well.
    using this approach, there does not seem to be a difference due to age!

  • @isaidhs
    @isaidhs 5 месяцев назад

    gold

  • @suhailshaikh5091
    @suhailshaikh5091 Год назад

    Hey Ajay, thanks a lot for making this video. Super helpful. Best video I came across on Causal inference.
    I have a question regarding Balanceness check between treatment and control group.
    Is it necessary to satisfy the balance criteria if I am using a ML model to predict the counterfactuals? Is it okay if there’s no balance between some confounders in Treatment and control group? Would really appreciate helping with this.

  • @stavs1792
    @stavs1792 2 года назад

    Great video! what tool do you use to create the presentation and the animations?

  • @GhemonA
    @GhemonA 2 года назад +1

    How do you create the Treatment and Control groups at 8:55?

  • @KnowNothingJohnSnow
    @KnowNothingJohnSnow 2 года назад

    wow!!!! your explaination is better than my epidemiology professor. thanks a lot!!! By the way, is there any recommand paper for RCT design r about Causal Inference ?

    • @CodeEmporium
      @CodeEmporium  2 года назад

      Thank you! As for specific resources, i put them in the description of the video. I don't think there is a single research paper that is the one size fits all for the topic, but a collection of these resources does paint a good picture. Also the next video's description had other resources from a Machine Learning perspective

    • @KnowNothingJohnSnow
      @KnowNothingJohnSnow 2 года назад

      @@CodeEmporium Thank u so much ! I learn a lot from your channel

  • @vaffapsychology6143
    @vaffapsychology6143 2 года назад

    Your voice is soo ..beautiful 😍

  • @karannchew2534
    @karannchew2534 6 месяцев назад

    "Control other effect through randomisation"

  • @1UniverseGames
    @1UniverseGames 2 года назад

    Sir, can you make a video on using DeepSpeed on Pytorch Cifar10? How to implement it on it.

    • @CodeEmporium
      @CodeEmporium  2 года назад

      I can look onto this and see what the most palatable format is for a video. Thanks for the suggestion

  • @markella3699
    @markella3699 2 года назад

    Would age in this case be an effect modifier?

  • @goldmandrummer
    @goldmandrummer Год назад

    The counterfactuals seem questionable... Is it really reasonable to say Sam would not get better with the treatment if he did get better without the treatment? That seems highly unlikely, doesn't it?...and the inverse for Rondo seems highly unlikely as well...
    I'm admittedly clueless about statistics but I'm always on the lookout for bad logic and this was a red flag for me.
    I don't mean to suggest a bad example on your part but rather that, in general, it seems there is a huge opening for error to sneak in through counterfactuals.

  • @02dddanny
    @02dddanny 2 года назад

    at 10:06 you mention that the age differences was large enough to warrant age to be labeled as a confounding variable. what exactly was the magnitude of difference that leads to that assumption? if the age means were 35 and 40, would that be a large enough difference? thanks.

    • @CodeEmporium
      @CodeEmporium  2 года назад

      You have 2 distribution of ages. You can conduct a statistical test to check if the 2 distributons are different. If significant, then yes.

  • @Anonymous-be4qf
    @Anonymous-be4qf Год назад

    50% recovered, RIP the other 50% elderly 😭

  • @user-wr4yl7tx3w
    @user-wr4yl7tx3w Год назад

    Can causal inference be applied to time series data?

  • @twoheadsy
    @twoheadsy Год назад

    David Cross, is that you?

  • @dato007
    @dato007 2 года назад

    This is missing statistical testing. All these are potentially non significant marginal results.

  • @uctube5502
    @uctube5502 Год назад

    Your face being a distraction is some sort of error, you should release one with it to find the counterfactual

  • @ExtraterrestrialIntelligence
    @ExtraterrestrialIntelligence 2 года назад

    I am a causality denier! I don't believe in causality. At least not the causality that we are familiar with. I think we need higher-order logic of at least the 69th degree to come up with an explanation for causality. I don't wear a tinfoil hat. I wear a quantum metamaterial protective helmet.

  • @AllUserNamesTaken111
    @AllUserNamesTaken111 2 года назад +1

    does this guy not know how to pronounce "causal"?