More in this series 👇 Intro to Causality: ua-cam.com/video/WqASiuM4a-A/v-deo.html Causal Discovery: ua-cam.com/video/tufdEUSjmNI/v-deo.html Intro to Causal Effects: ua-cam.com/video/BOPOX_mTS0g/v-deo.html Propensity Scores: ua-cam.com/video/dm-BWjyYQpw/v-deo.html Do-operator: ua-cam.com/video/dejZzJIZdow/v-deo.html DAGs: ua-cam.com/video/ASU5HG5EqTM/v-deo.html Regression techniques: ua-cam.com/video/O72uByJlnMw/v-deo.html Towards Data Science article: towardsdatascience.com/causal-inference-962ae97cefda?sk=d68d5191fdb00d3fee47aaa43ed48f3d
Thanks Zhanbolat! That’s a great suggestion. I haven’t done much with Bayesian networks in Python, but it overlaps with causal inference. It could fit in nicely in an appendix to this series.
9:40 Average Causal Effect is 0.2 = Having degree increase chance of earning over 50k Confounder: Age Treatment: HavingDegreeOrNot Response: EarnOver50kOrNot Confounder: OverallScore Treatment: HaveSolutionOrNot Response: LocalScore
Great book! Definitely meant for a general audience. I talk a bit more about the do-operator in another (technical) video: ua-cam.com/video/dejZzJIZdow/v-deo.html
Not necessarily. This will depend on the method you are using. For instance, the T-learner method here can handle non-binary targets. However, using binary targets often simplifies the analysis.
In what ways do you think causal inference and bayesian inference are related and unrelated? Is the latter only addressing correlation and therefore makes no causal statement.
Id say these are 2 different animals. Bayesian inference is a particular paradigm for doing inference. On the other hand, causal inference is not so much a particular approach to inference, rather it is any inference where the goal is to answer a question involving cause and effect. From this view, one could even use Bayesian inference to do causal inference Hope that helps!
This was the best video so far about Casual Inference...so simple and to the point.. Thank you
Thanks for the comment, glad it was helpful!
This was an awesome introduction! I've looked into the connection of Graph Neural Networks and Causality and this was a superb introduction! Subbed
Thanks so much! Glad it was helpful
More in this series 👇
Intro to Causality: ua-cam.com/video/WqASiuM4a-A/v-deo.html
Causal Discovery: ua-cam.com/video/tufdEUSjmNI/v-deo.html
Intro to Causal Effects: ua-cam.com/video/BOPOX_mTS0g/v-deo.html
Propensity Scores: ua-cam.com/video/dm-BWjyYQpw/v-deo.html
Do-operator: ua-cam.com/video/dejZzJIZdow/v-deo.html
DAGs: ua-cam.com/video/ASU5HG5EqTM/v-deo.html
Regression techniques: ua-cam.com/video/O72uByJlnMw/v-deo.html
Towards Data Science article: towardsdatascience.com/causal-inference-962ae97cefda?sk=d68d5191fdb00d3fee47aaa43ed48f3d
One of the best videos on the topic, and the examples are so easy to follow!
Thanks! Glad it was clear
I can't understand the low value of the views counter under this video. Because the way you explain this material is so good!
lol thanks! Glad you liked it :)
Another great vid!
Coming for that first spot
You will get another chance soon
Well explained. Congrats!
Super informational! Thanks for sharing Shaw!
Thanks, happy to share!
Thank you a lot Shaw! Great video. Will you show some examples of Bayesian Network in python? This example was very helpful
.
Thanks Zhanbolat! That’s a great suggestion. I haven’t done much with Bayesian networks in Python, but it overlaps with causal inference. It could fit in nicely in an appendix to this series.
@@ShawhinTalebi thank you very much! It will be very helpful!
9:40
Average Causal Effect is 0.2 = Having degree increase chance of earning over 50k
Confounder: Age
Treatment: HavingDegreeOrNot Response: EarnOver50kOrNot
Confounder: OverallScore
Treatment: HaveSolutionOrNot
Response: LocalScore
Great video! Thank you for sharing.
Thank you so much. I read "The book of why", in which the do-operator is mentioned, but it's not explained on how to use it!
Great book! Definitely meant for a general audience.
I talk a bit more about the do-operator in another (technical) video: ua-cam.com/video/dejZzJIZdow/v-deo.html
Hi is it possible to use causal inference for feature selection in ML?
Interesting question. I haven't come across anything like that. Perhaps ideas from causal discovery could be used.
Great content.
Thank you!
Hahaha the example problem really pokes my anxiety. 😂😂😂
😂😂I'm sorry!
Doubt: Outcome should always be a binary column?
Not necessarily. This will depend on the method you are using.
For instance, the T-learner method here can handle non-binary targets. However, using binary targets often simplifies the analysis.
In what ways do you think causal inference and bayesian inference are related and unrelated? Is the latter only addressing correlation and therefore makes no causal statement.
Id say these are 2 different animals. Bayesian inference is a particular paradigm for doing inference. On the other hand, causal inference is not so much a particular approach to inference, rather it is any inference where the goal is to answer a question involving cause and effect. From this view, one could even use Bayesian inference to do causal inference
Hope that helps!
Love it!!
Glad you like it!
1st
True according to Python
bad teaching.
I'm always looking to improve. What issues do you see here?