Very well described. Thank You!

Thank you for your video. give me the answer please.... I have an exam tomorrow... okay bye....

Great content! I am studying in the university now Causality subject and your youtube videos just give me the right amount of information to understand the topics that were more abstract to me (given just by the definitions). I just got confused though by the slide on the timestamp 0:25. You talk there about association (red dotted arrow), and it seems like it should two a two sided arrow, since association is purely statistical notion and doesn't have a direction. So I guess its either a typo or I misinterpreted something 😁

Question: Hi, I don't know if this question is too late but here it goes. On slide 9, how does having the additional observational data increase the number of interventions from (N-1) to N instead of decreasing it. I understand that one of those 'N' interventions is the observational data. So is this observational data useless if we still need (N-1) more single interventions. I mean more data should decrease the number of additional single node interventions necessary right?

ASSUME A TABLE CAN BLOCK the impact from THE C to X(7::45)

could you be kind to to share stata sytax for counterfactual prediction in the Multinomial endogenous switching model- you can use my email that i will send if I learn you are willing to help me

Really great work by you to make a complex set of topics in casual inference accessible to everyone

Thanks for the video. It looks like in the 8:36 example the difference between counterfactual and potential outcome is that in counterfactual U is random but in potential outcome case , where things could be resolved by do calculus, U is an unknown predetermined parameter. Am I correct?

For real man, thank you so much for everything you have done!!. You have made this stuff really accessible for folks like me!

I think the middle notation is more intuitive to read.

How would the immorality change if we were to condition on X2? Or is the argument at 29:33 about conditioning on X2? I thought If we were to condition on X2, P(x1) and P(x2) were to be dependent?

the gunshot treatment. lmao

I SAW THE DEATH STAR!

Can someone explain at 2:00 why Brady wrote P(x_3) instead of P(x_3 | x_1)? Did he use the fact that x_1 and x_3 are independent? Isn't he already trying to prove that they are independent by getting to 2:47? Thank you!

Neal, I would like to propose an alternative explanation to yours using a scientific realism perspective on causal inference. You said, In scenario 1, treatment has an effect on the condition which has an effect on the probability of the outcome. And in scenario 2: the condition has an effect on the treatment which has an effect on the probability of the outcome. The treatment is the mechanism and the condition, the context or circumstances. I would argue the mechanism is the cause and the condition is the trigger. In scenario one, the outcome is considered in the basis of the mechanism alone and in scenario 2, the outcome is deduced on the basis of the trigger condition of the mechanism. Mechanisms are always triggered in context to create outcomes. Mechanism are causes but their outcomes are always shaped by context. I would also add that some mechanisms may be hidden, meaning there are other unobservable mechanisms - which under experimental conditions (!closed systems) can be controlled. In open systems, the outcome may not be easily predictable because of these unobservable mechanisms. In you Mr example, these mechanisms could be environmental - co-morbidities, contra-indications, emotional state, age related etc. So I would be cautious about relying on data driven inferencing alone.

Neal, I would like to propose an alternative explanation to yours using a scientific realism perspective on causal inference. You said, In scenario 1, treatment has an effect on the condition which has an effect on the probability of the outcome. And in scenario 2: the condition has an effect on the treatment which has an effect on the probability of the outcome. The treatment is the mechanism and the condition, the context or circumstances. I would argue the mechanism is the cause and the condition is the trigger. In scenario one, the outcome is considered in the basis of the mechanism alone and in scenario 2, the outcome is deduced on the basis of the trigger condition of the mechanism. Mechanisms are always triggered in context to create outcomes. Mechanism are causes but their outcomes are always shaped by context. I would also add that some mechanisms may be hidden, meaning there are other unobservable mechanisms - which under experimental conditions (closed systems) can be controlled. In open systems, the outcome may not be easily predictable because of these unobservable mechanisms. In you Mr example, these mechanisms could be environmental - co-morbidities, contra-indications, emotional state, age related etc. So I would be cautious about relying on data driven inferencing alone.

Thanks, I got so much intuition in the course! It must have been a lot of work for you to put it together!

I note that from the python code on github, if you condition on Age alone the exact effect is recovered i.e 1.05. Is this because proteinuria is a collider? Does this mean that we should not include colliders in the linear regression?

Was there a followup question to “Assistance to the poor or welfare question?” … something like …. “Would your opinion change if you were told that “Welfare” is just another term for “Assistance to the poor” ?

Is there 5 other possible graphs in the Markov Equivalence class of the final question?

very interesting examples

Turn on auto-subtitles please

Please tell me if I understood the idea correctly - the syntax using do describes the same thing as the syntax using potential outcome. It's just a different way of presenting the same problem (identification)

it's amazing

At 2:53, we can prove all this using the minimality assumption, right?

We can model the mathematical expectation with different models, right? Even those that cannot be interpreted. Will the calculation of the casual effect be correct in this case?

Basic clarification question - X is subset is W? And can be same as W as well, correct? W is set of all covariates and X is set of variables on which treatment effect is conditioned.

Here I was, excited to learn about the Rule 1 of do-calculus, and then seeing a bunch of symbols I don't understand in the next moment. I think a whole lot of people will understand better if you gave them the intuition first and not the symbols and notations, Brady.

Thank you.

Is the answer to the question at the end 1? After drawing the essential graph, I found only one edge that is undirected (A-B), and if I am not wrong, since we know from the essential graph that there is an edge between A and B, intervening for either one of them should reveal the direction of the edge.

very nice work

thank you so much for delivering this course!

You explained complex concepts with such great clarity. Thank you for the invaluable series!

Wonderful explanations

would you attach the data for exercise

Amazing introduction to the subject. Explained the generic problem and an intuitive solution. I now feel inspired to read the rest of the units, and understand the details.

At 9:00, how does the second condition (W does not contain any descendants of T) relate to the d-separation statement? It seems like conditioning on a descendant of T would not affect d-separation in G_{\underline{T}}, or can someone provide an example where it does?

This format should really be the future direction of education. It just makes so much more sense.

2:11 The first rule of do-calculus is you do NOT talk about do-calculus.

Thanks for helping me with my homework you are a hero :)

Great series, Thank you!

I lost it at the reddit post 😭😭😭😭

Are not you confusing Treatment with condition here, and more prominently in previous video? How can you assume potential outcome Y(o) Y(1) are independent of treatment ? T, Then what's the point on running a trial ? You might assume that PO are independent of "Condition (C)", so that sleeping with shoes on and off is not actually a factor, and we will know what the treatment T (Taking the pill say) causal effect is in general ? That is equivalent to say that what is the use of randomizing folks among control and treatment groups, when confounder based upon which you were trying to randomize has no effect on outcome in general (which is correct statement)....so why even bother randomizing? Let me know what you think ? Most probably, we are abusing the term "treatment" here, at times using it for "intervention" and at times confusing it with "confounders/conditions/covariates"

But wouldn't it make sense for something to have the ability to give an outcome that doesn't always follow the expectation of treatment without violating consistency? Like if you get treatment (test prep course) you are expected to score higher than 50. But there will be ppl who do take the prep course and still score below 50 how would that be a violation of consistency? Thank you.

This is awesome! Thank you!

Covid-27 got me 😂😂😂

Hey! Can someone please help me understand if it is only the disproportional quantities of some biasing features that are making the groups non comparable? In other words: If I force both of my groups (sleeping with and without shoes on) to have the same proportion of drunk people, will I have my groups comparable?

Just a minor question, wouldn't a more accurate DAG in minute 23:10 be like: W -> { T, Y, e(W) }; T -> { Y } indicating that e(W) is a descendant of W (constructed from it). Therefore, conditioning on a descendant of W, in this case e(W), still closes the backdoor path, but partially.

Nice meme at 6:54 😉

How did you generat these slides? What software? The animations and graphics are so cool and it would be great when presenting my PhD work!