-
Causality [Simply explained]
In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect relationship between two variables.
A common mistake in the interpretation of statistics is that when a correlation exists it is immediately assumed to be a causal relationship.
There are two prerequisites for causality:
First, there is a significant relationship, that is, a significant Correlation.
The second condition can be satisfied in two ways.
First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B.
Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship go...
published: 15 Feb 2022
-
Correlation vs Causation (Statistics)
Correlation is used to understand the relationship between variables. However, correlation does not imply causation.
published: 13 Sep 2022
-
Section 5.1 Causal Relationships: The Basics
An introduction to the terminology and concepts used when talking about causal relationships.
published: 24 Oct 2018
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4 Important Casual Relationship Rules That You Should Keep in Mind
published: 17 Jul 2019
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Causal Relationships
Thinking slides: https://docs.google.com/presentation/d/1AlYNpDAvWXTuuFdz2jX3WmxEeYJY9uJEEtJdE5japGs/template/preview
The Wonder of Science:
https://thewonderofscience.com/mlccc23
Thinking in Causation - Level 3 - Causal Relationships
In this video Paul Andersen shows conceptual thinking in a mini-lesson on causal relationships. Two examples are included in the video and two additional examples are included in the linked thinking slides.
TERMS:
Cause - a thing that gives rise to an event
Effect - an event
Relationship - interconnection between parts of a system
This progression is based on the Crosscutting Concept elements from the NRC document A Framework for K-12 Science Education. "Cause and effect relationships are routinely identified."
Source: https://www.nextgenscience.org...
published: 01 Feb 2021
-
When she wants a "casual" relationship
She hit him with the uno reverse card
Shameless Patreon plug: https://www.patreon.com/ContentMachine
Comment "Mazzy got played" if you want him to reveal his actual body count (it's between -1 and 3)
published: 22 Dec 2023
-
What Does a Casual Relationship Mean to a Guy?
Irresistible Texts - https://commitmentconnection.com/texts/?el=ytvideo
4 Proven Ways to Attract the Man You Truly Desire - https://matthewcoast.com/?el=ytvideo
The Devotion Switch - https://commitmentconnection.com/devotion/?el=ytvideo
Feminine Enchantment → https://feminineenchantment.com/?el=ytvideo
The Obsession Formula - https://commitmentconnection.com/obsession/?el=ytvideo
Long Distance Allure - https://commitmentconnection.com/long-distance-allure/?el=ytvideo
Get your ex back - https://commitmentconnection.com/restart-your-relationship/?el=ytvideo
published: 17 Mar 2018
-
CRITICAL THINKING - Fundamentals: Correlation and Causation
In this Wireless Philosophy video, Paul Henne (Duke University) explains the difference between correlation and causation.
Subscribe!
http://bit.ly/1vz5fK9
More on Paul Henne:
http://bit.ly/29alRyb
----
Wi-Phi @ YouTube:
http://bit.ly/1PX0hLu
Wi-Phi @ Khan Academy:
http://bit.ly/1nQJcF7
Twitter:
https://twitter.com/wirelessphi
Instagram:
@wiphiofficial
Facebook:
http://on.fb.me/1XC2tx3
----
Help us caption & translate this video!
http://amara.org/v/4tzX/
published: 10 Mar 2017
-
Meaningful Causal Aggregation and Paradoxical Confounding | Yuchen Zhu
Portal is the home of the TechBio community. Join for more details on this talk and to connect with the speakers: https://portal.valencelabs.com/care
Summary: In aggregated variables the impact of interventions is typically ill-defined because different micro-level realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causality on aggregated variables can turn unconfounded causal relations into confounded ones and vice versa, depending on the respective micro-level realization. We argue that it is practically infeasible to only use aggregated causal systems when we are free from this ill-definedness. Instead, we need to accept that macro causal relations are typically defined only with reference to t...
published: 26 Mar 2024
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Casual Relationships | Stand Up Comedy by Urooj Ashfaq
In this video I talk about casual relationships casually. Most of us have been there and for those of us who haven't you can live vicariously through this video it'll be over in less than 6 minutes coincidentally that's also how long most casual relationships last.
I had different priorities prior to this pandemic, I hope you're all safe and taking care of yourselves.
CREDITS:
Venue Courtesy: That Comedy Club, Bangalore
Edit & Grade: Karan Asnani
Post Production: Myoho Films (@Myoho Films )
Video & Sound Recording: Rakesh UP & Team (bangalore)
Email: itsrakeshup@gmail.com
Sound Mixing & Mastering: Sreejith Menon
you can follow me at:
INSTAGRAM: https://www.instagram.com/uroojashfaq/
FACEBOOK: @rougeaf
TWITTER: LEAVING IT WAS THE BEST DECISION I EVER MADE.
published: 09 Jun 2020
7:46
Causality [Simply explained]
In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect ...
In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect relationship between two variables.
A common mistake in the interpretation of statistics is that when a correlation exists it is immediately assumed to be a causal relationship.
There are two prerequisites for causality:
First, there is a significant relationship, that is, a significant Correlation.
The second condition can be satisfied in two ways.
First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B.
Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship goes.
If neither of the two is true, i.e. there is neither a temporal order nor can the causality be justified by a well-founded theory, then we can only speak of a relationship, but never of causality, i.e. it cannot be said that variable A influences variable B or vice versa.
More Information about Causality:
https://datatab.net/tutorial/causality
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
https://wn.com/Causality_Simply_Explained
In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect relationship between two variables.
A common mistake in the interpretation of statistics is that when a correlation exists it is immediately assumed to be a causal relationship.
There are two prerequisites for causality:
First, there is a significant relationship, that is, a significant Correlation.
The second condition can be satisfied in two ways.
First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B.
Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship goes.
If neither of the two is true, i.e. there is neither a temporal order nor can the causality be justified by a well-founded theory, then we can only speak of a relationship, but never of causality, i.e. it cannot be said that variable A influences variable B or vice versa.
More Information about Causality:
https://datatab.net/tutorial/causality
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
- published: 15 Feb 2022
- views: 33457
2:11
Correlation vs Causation (Statistics)
Correlation is used to understand the relationship between variables. However, correlation does not imply causation.
Correlation is used to understand the relationship between variables. However, correlation does not imply causation.
https://wn.com/Correlation_Vs_Causation_(Statistics)
Correlation is used to understand the relationship between variables. However, correlation does not imply causation.
- published: 13 Sep 2022
- views: 47749
3:50
Section 5.1 Causal Relationships: The Basics
An introduction to the terminology and concepts used when talking about causal relationships.
An introduction to the terminology and concepts used when talking about causal relationships.
https://wn.com/Section_5.1_Causal_Relationships_The_Basics
An introduction to the terminology and concepts used when talking about causal relationships.
- published: 24 Oct 2018
- views: 5068
5:56
Causal Relationships
Thinking slides: https://docs.google.com/presentation/d/1AlYNpDAvWXTuuFdz2jX3WmxEeYJY9uJEEtJdE5japGs/template/preview
The Wonder of Science:
https://thewonderof...
Thinking slides: https://docs.google.com/presentation/d/1AlYNpDAvWXTuuFdz2jX3WmxEeYJY9uJEEtJdE5japGs/template/preview
The Wonder of Science:
https://thewonderofscience.com/mlccc23
Thinking in Causation - Level 3 - Causal Relationships
In this video Paul Andersen shows conceptual thinking in a mini-lesson on causal relationships. Two examples are included in the video and two additional examples are included in the linked thinking slides.
TERMS:
Cause - a thing that gives rise to an event
Effect - an event
Relationship - interconnection between parts of a system
This progression is based on the Crosscutting Concept elements from the NRC document A Framework for K-12 Science Education. "Cause and effect relationships are routinely identified."
Source: https://www.nextgenscience.org/
https://wn.com/Causal_Relationships
Thinking slides: https://docs.google.com/presentation/d/1AlYNpDAvWXTuuFdz2jX3WmxEeYJY9uJEEtJdE5japGs/template/preview
The Wonder of Science:
https://thewonderofscience.com/mlccc23
Thinking in Causation - Level 3 - Causal Relationships
In this video Paul Andersen shows conceptual thinking in a mini-lesson on causal relationships. Two examples are included in the video and two additional examples are included in the linked thinking slides.
TERMS:
Cause - a thing that gives rise to an event
Effect - an event
Relationship - interconnection between parts of a system
This progression is based on the Crosscutting Concept elements from the NRC document A Framework for K-12 Science Education. "Cause and effect relationships are routinely identified."
Source: https://www.nextgenscience.org/
- published: 01 Feb 2021
- views: 6085
1:29
When she wants a "casual" relationship
She hit him with the uno reverse card
Shameless Patreon plug: https://www.patreon.com/ContentMachine
Comment "Mazzy got played" if you want him to reveal his ...
She hit him with the uno reverse card
Shameless Patreon plug: https://www.patreon.com/ContentMachine
Comment "Mazzy got played" if you want him to reveal his actual body count (it's between -1 and 3)
https://wn.com/When_She_Wants_A_Casual_Relationship
She hit him with the uno reverse card
Shameless Patreon plug: https://www.patreon.com/ContentMachine
Comment "Mazzy got played" if you want him to reveal his actual body count (it's between -1 and 3)
- published: 22 Dec 2023
- views: 484019
6:18
What Does a Casual Relationship Mean to a Guy?
Irresistible Texts - https://commitmentconnection.com/texts/?el=ytvideo
4 Proven Ways to Attract the Man You Truly Desire - https://matthewcoast.com/?el=ytvide...
Irresistible Texts - https://commitmentconnection.com/texts/?el=ytvideo
4 Proven Ways to Attract the Man You Truly Desire - https://matthewcoast.com/?el=ytvideo
The Devotion Switch - https://commitmentconnection.com/devotion/?el=ytvideo
Feminine Enchantment → https://feminineenchantment.com/?el=ytvideo
The Obsession Formula - https://commitmentconnection.com/obsession/?el=ytvideo
Long Distance Allure - https://commitmentconnection.com/long-distance-allure/?el=ytvideo
Get your ex back - https://commitmentconnection.com/restart-your-relationship/?el=ytvideo
https://wn.com/What_Does_A_Casual_Relationship_Mean_To_A_Guy
Irresistible Texts - https://commitmentconnection.com/texts/?el=ytvideo
4 Proven Ways to Attract the Man You Truly Desire - https://matthewcoast.com/?el=ytvideo
The Devotion Switch - https://commitmentconnection.com/devotion/?el=ytvideo
Feminine Enchantment → https://feminineenchantment.com/?el=ytvideo
The Obsession Formula - https://commitmentconnection.com/obsession/?el=ytvideo
Long Distance Allure - https://commitmentconnection.com/long-distance-allure/?el=ytvideo
Get your ex back - https://commitmentconnection.com/restart-your-relationship/?el=ytvideo
- published: 17 Mar 2018
- views: 61230
7:09
CRITICAL THINKING - Fundamentals: Correlation and Causation
In this Wireless Philosophy video, Paul Henne (Duke University) explains the difference between correlation and causation.
Subscribe!
http://bit.ly/1vz5fK9
Mo...
In this Wireless Philosophy video, Paul Henne (Duke University) explains the difference between correlation and causation.
Subscribe!
http://bit.ly/1vz5fK9
More on Paul Henne:
http://bit.ly/29alRyb
----
Wi-Phi @ YouTube:
http://bit.ly/1PX0hLu
Wi-Phi @ Khan Academy:
http://bit.ly/1nQJcF7
Twitter:
https://twitter.com/wirelessphi
Instagram:
@wiphiofficial
Facebook:
http://on.fb.me/1XC2tx3
----
Help us caption & translate this video!
http://amara.org/v/4tzX/
https://wn.com/Critical_Thinking_Fundamentals_Correlation_And_Causation
In this Wireless Philosophy video, Paul Henne (Duke University) explains the difference between correlation and causation.
Subscribe!
http://bit.ly/1vz5fK9
More on Paul Henne:
http://bit.ly/29alRyb
----
Wi-Phi @ YouTube:
http://bit.ly/1PX0hLu
Wi-Phi @ Khan Academy:
http://bit.ly/1nQJcF7
Twitter:
https://twitter.com/wirelessphi
Instagram:
@wiphiofficial
Facebook:
http://on.fb.me/1XC2tx3
----
Help us caption & translate this video!
http://amara.org/v/4tzX/
- published: 10 Mar 2017
- views: 279319
55:34
Meaningful Causal Aggregation and Paradoxical Confounding | Yuchen Zhu
Portal is the home of the TechBio community. Join for more details on this talk and to connect with the speakers: https://portal.valencelabs.com/care
Summary: ...
Portal is the home of the TechBio community. Join for more details on this talk and to connect with the speakers: https://portal.valencelabs.com/care
Summary: In aggregated variables the impact of interventions is typically ill-defined because different micro-level realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causality on aggregated variables can turn unconfounded causal relations into confounded ones and vice versa, depending on the respective micro-level realization. We argue that it is practically infeasible to only use aggregated causal systems when we are free from this ill-definedness. Instead, we need to accept that macro causal relations are typically defined only with reference to the micro states. On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions. We also discuss generalizations of this observation.
Speaker: Yuchen Zhu
Twitter Chandler: https://twitter.com/chandlersquires
Twitter Dhanya: https://twitter.com/dhanya_sridhar
Twitter Jason: https://twitter.com/jasonhartford
~
Chapters
00:00 - Introduction
04:41 - Challenges of Causal Abstraction
14:38 - Macro Intervention and Micro-realism
41:28 - Linear Gaussian Example
54:05 - Summary
https://wn.com/Meaningful_Causal_Aggregation_And_Paradoxical_Confounding_|_Yuchen_Zhu
Portal is the home of the TechBio community. Join for more details on this talk and to connect with the speakers: https://portal.valencelabs.com/care
Summary: In aggregated variables the impact of interventions is typically ill-defined because different micro-level realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causality on aggregated variables can turn unconfounded causal relations into confounded ones and vice versa, depending on the respective micro-level realization. We argue that it is practically infeasible to only use aggregated causal systems when we are free from this ill-definedness. Instead, we need to accept that macro causal relations are typically defined only with reference to the micro states. On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions. We also discuss generalizations of this observation.
Speaker: Yuchen Zhu
Twitter Chandler: https://twitter.com/chandlersquires
Twitter Dhanya: https://twitter.com/dhanya_sridhar
Twitter Jason: https://twitter.com/jasonhartford
~
Chapters
00:00 - Introduction
04:41 - Challenges of Causal Abstraction
14:38 - Macro Intervention and Micro-realism
41:28 - Linear Gaussian Example
54:05 - Summary
- published: 26 Mar 2024
- views: 46
5:53
Casual Relationships | Stand Up Comedy by Urooj Ashfaq
In this video I talk about casual relationships casually. Most of us have been there and for those of us who haven't you can live vicariously through this video...
In this video I talk about casual relationships casually. Most of us have been there and for those of us who haven't you can live vicariously through this video it'll be over in less than 6 minutes coincidentally that's also how long most casual relationships last.
I had different priorities prior to this pandemic, I hope you're all safe and taking care of yourselves.
CREDITS:
Venue Courtesy: That Comedy Club, Bangalore
Edit & Grade: Karan Asnani
Post Production: Myoho Films (@Myoho Films )
Video & Sound Recording: Rakesh UP & Team (bangalore)
Email: itsrakeshup@gmail.com
Sound Mixing & Mastering: Sreejith Menon
you can follow me at:
INSTAGRAM: https://www.instagram.com/uroojashfaq/
FACEBOOK: @rougeaf
TWITTER: LEAVING IT WAS THE BEST DECISION I EVER MADE.
https://wn.com/Casual_Relationships_|_Stand_Up_Comedy_By_Urooj_Ashfaq
In this video I talk about casual relationships casually. Most of us have been there and for those of us who haven't you can live vicariously through this video it'll be over in less than 6 minutes coincidentally that's also how long most casual relationships last.
I had different priorities prior to this pandemic, I hope you're all safe and taking care of yourselves.
CREDITS:
Venue Courtesy: That Comedy Club, Bangalore
Edit & Grade: Karan Asnani
Post Production: Myoho Films (@Myoho Films )
Video & Sound Recording: Rakesh UP & Team (bangalore)
Email: itsrakeshup@gmail.com
Sound Mixing & Mastering: Sreejith Menon
you can follow me at:
INSTAGRAM: https://www.instagram.com/uroojashfaq/
FACEBOOK: @rougeaf
TWITTER: LEAVING IT WAS THE BEST DECISION I EVER MADE.
- published: 09 Jun 2020
- views: 7296531