#causally — Public Fediverse posts

'Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables', by Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang.

http://jmlr.org/papers/v25/23-1052.html

#causal #causally #covariance

'Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables', by Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang.

http://jmlr.org/papers/v25/23-1052.html

#causal #causally #covariance

'Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables', by Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang.

http://jmlr.org/papers/v25/23-1052.html

#causal #causally #covariance

'Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables', by Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang.

http://jmlr.org/papers/v25/23-1052.html

#causal #causally #covariance

'Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables', by Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang.

http://jmlr.org/papers/v25/23-1052.html

#causal #causally #covariance

'Optimization-based Causal Estimation from Heterogeneous Environments', by Mingzhang Yin, Yixin Wang, David M. Blei.

http://jmlr.org/papers/v25/21-1028.html

#causal #causally #causality

'Optimization-based Causal Estimation from Heterogeneous Environments', by Mingzhang Yin, Yixin Wang, David M. Blei.

http://jmlr.org/papers/v25/21-1028.html

#causal #causally #causality

'Optimization-based Causal Estimation from Heterogeneous Environments', by Mingzhang Yin, Yixin Wang, David M. Blei.

http://jmlr.org/papers/v25/21-1028.html

#causal #causally #causality

'Optimization-based Causal Estimation from Heterogeneous Environments', by Mingzhang Yin, Yixin Wang, David M. Blei.

http://jmlr.org/papers/v25/21-1028.html

#causal #causally #causality

'Optimization-based Causal Estimation from Heterogeneous Environments', by Mingzhang Yin, Yixin Wang, David M. Blei.

http://jmlr.org/papers/v25/21-1028.html

#causal #causally #causality

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

'Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding', by Justin Grimmer, Dean Knox, Brandon Stewart.

http://jmlr.org/papers/v24/21-0515.html

#confounders #confounder #causally

@BartoszMilewski
> <em> [...] decide if things are #causally connected. Is it enough that we observe them in sequence over and over again? </em>

Btw., this requires that "they" are

- individually ("time for time") distinguishable, and also

- "over and over" classifiable (being "of one kind, or the other" etc.)

Also required (or to consider):
The "effect thing" should never have been found without prior occurence of the "cause thing". (You might call that "the simplest/essential model".)

@BartoszMilewski
> <em> [...] decide if things are #causally connected. Is it enough that we observe them in sequence over and over again? </em>

Btw., this requires that "they" are

- individually ("time for time") distinguishable, and also

- "over and over" classifiable (being "of one kind, or the other" etc.)

Also required (or to consider):
The "effect thing" should never have been found without prior occurence of the "cause thing". (You might call that "the simplest/essential model".)

@BartoszMilewski
> <em> [...] decide if things are #causally connected. Is it enough that we observe them in sequence over and over again? </em>

Btw., this requires that "they" are

- individually ("time for time") distinguishable, and also

- "over and over" classifiable (being "of one kind, or the other" etc.)

Also required (or to consider):
The "effect thing" should never have been found without prior occurence of the "cause thing". (You might call that "the simplest/essential model".)

@BartoszMilewski
> <em> [...] decide if things are #causally connected. Is it enough that we observe them in sequence over and over again? </em>

Btw., this requires that "they" are

- individually ("time for time") distinguishable, and also

- "over and over" classifiable (being "of one kind, or the other" etc.)

Also required (or to consider):
The "effect thing" should never have been found without prior occurence of the "cause thing". (You might call that "the simplest/essential model".)

@BartoszMilewski
> <em> [...] decide if things are #causally connected. Is it enough that we observe them in sequence over and over again? </em>

Btw., this requires that "they" are

- individually ("time for time") distinguishable, and also

- "over and over" classifiable (being "of one kind, or the other" etc.)

Also required (or to consider):
The "effect thing" should never have been found without prior occurence of the "cause thing". (You might call that "the simplest/essential model".)

@gideonk @AllenNeuroLab @karihoffman @charanranganath

For me, the key distinction is whether the #memory is encoded with information about a #narrative of one's experiences, i.e. is the memory placed #spatially, #temporally, and #causally within an account of your trajectory through life (i.e. relative to other #episodic memories)?

But, per AllenLab's point, the mixture of these things will be different for different memories, so one could imagine a more refined taxonomy.

@gideonk @AllenNeuroLab @karihoffman @charanranganath

For me, the key distinction is whether the #memory is encoded with information about a #narrative of one's experiences, i.e. is the memory placed #spatially, #temporally, and #causally within an account of your trajectory through life (i.e. relative to other #episodic memories)?

But, per AllenLab's point, the mixture of these things will be different for different memories, so one could imagine a more refined taxonomy.

@gideonk @AllenNeuroLab @karihoffman @charanranganath

For me, the key distinction is whether the #memory is encoded with information about a #narrative of one's experiences, i.e. is the memory placed #spatially, #temporally, and #causally within an account of your trajectory through life (i.e. relative to other #episodic memories)?

But, per AllenLab's point, the mixture of these things will be different for different memories, so one could imagine a more refined taxonomy.

@gideonk @AllenNeuroLab @karihoffman @charanranganath

For me, the key distinction is whether the #memory is encoded with information about a #narrative of one's experiences, i.e. is the memory placed #spatially, #temporally, and #causally within an account of your trajectory through life (i.e. relative to other #episodic memories)?

But, per AllenLab's point, the mixture of these things will be different for different memories, so one could imagine a more refined taxonomy.

@gideonk @AllenNeuroLab @karihoffman @charanranganath

For me, the key distinction is whether the #memory is encoded with information about a #narrative of one's experiences, i.e. is the memory placed #spatially, #temporally, and #causally within an account of your trajectory through life (i.e. relative to other #episodic memories)?

But, per AllenLab's point, the mixture of these things will be different for different memories, so one could imagine a more refined taxonomy.