Zach Shahn
@zshahn.bsky.social
Causal inference, assistant professor at CUNY SPH. All likes are endorsements, but maybe by my 3 year old who stole my phone.
And I now see how this doesn’t depend on intervention. Eg conditioning on being on earth induces associations between variables related to falling explained mathematically by Newton. So I think I fully understand now, thanks for bearing with the stream of consciousness if you made it this far!
October 26, 2025 at 5:14 PM
And I now see how this doesn’t depend on intervention. Eg conditioning on being on earth induces associations between variables related to falling explained mathematically by Newton. So I think I fully understand now, thanks for bearing with the stream of consciousness if you made it this far!
(Didn’t Google Boyle’s law, hope I got it right)
October 26, 2025 at 4:44 PM
(Didn’t Google Boyle’s law, hope I got it right)
I guess it doesn’t need to be as magical as fate. You could imagine presetting a machine to maintain gas pressure at some level however it needs to. Then temperature and volume become counterfactually related. And P=TV is a mathematical non-mechanistic explanation like you discuss in the paper
October 26, 2025 at 4:43 PM
I guess it doesn’t need to be as magical as fate. You could imagine presetting a machine to maintain gas pressure at some level however it needs to. Then temperature and volume become counterfactually related. And P=TV is a mathematical non-mechanistic explanation like you discuss in the paper
Ok, but now I’m back to my original understanding that the distinguishing feature of pre-selection is that it occurs via intervention and induces association via “fate” as in your fairy godmother example
October 26, 2025 at 3:02 PM
Ok, but now I’m back to my original understanding that the distinguishing feature of pre-selection is that it occurs via intervention and induces association via “fate” as in your fairy godmother example
Ok, but C is in the past of E and D, the exposure and outcome of interest? I think I'm being dense, so I won't ask you to explain again!
October 25, 2025 at 10:49 PM
Ok, but C is in the past of E and D, the exposure and outcome of interest? I think I'm being dense, so I won't ask you to explain again!
In M-bias, you’re conditioning on C, which is something pre-baseline like an eligibility criterion for a study. But it doesn’t arise from actually intervening to set C. Is that what makes predecessor bias different?
October 25, 2025 at 8:56 PM
In M-bias, you’re conditioning on C, which is something pre-baseline like an eligibility criterion for a study. But it doesn’t arise from actually intervening to set C. Is that what makes predecessor bias different?
Ha, I don’t think anything’s wrong, but I think M-bias from conditioning on pretreatment variables is one common case of what you’re describing
October 25, 2025 at 2:50 AM
Ha, I don’t think anything’s wrong, but I think M-bias from conditioning on pretreatment variables is one common case of what you’re describing
Yeah I was asking about your pinned post paper, read it with great interest
October 24, 2025 at 8:01 PM
Yeah I was asking about your pinned post paper, read it with great interest
Does predecessor bias have to come from a 'fated collider' that seems to maybe be a uniquely quantum thing? Or do you use the term to refer to any setting where conditioning on a pre-intervention variable induces an association? We call the latter case 'M-bias' journals.lww.com/epidem/abstr....
journals.lww.com
October 24, 2025 at 3:36 PM
Does predecessor bias have to come from a 'fated collider' that seems to maybe be a uniquely quantum thing? Or do you use the term to refer to any setting where conditioning on a pre-intervention variable induces an association? We call the latter case 'M-bias' journals.lww.com/epidem/abstr....
Causal people are usually interested in causal effects and would want to adjust away what we would call that spurious association. So we would just call it confounding adjustment. Maybe more descriptive people like demographers have a word for adjusting for confounding when you don’t want to
October 24, 2025 at 5:04 AM
Causal people are usually interested in causal effects and would want to adjust away what we would call that spurious association. So we would just call it confounding adjustment. Maybe more descriptive people like demographers have a word for adjusting for confounding when you don’t want to
Forgot to say, 'we' is me and my phd advisor David Madigan, who took a break from being a provost to do research again. Nice reunion!
October 16, 2025 at 2:47 AM
Forgot to say, 'we' is me and my phd advisor David Madigan, who took a break from being a provost to do research again. Nice reunion!
Next, we ask 'when will the key assumptions be satisfied'? We argue that under a causal pie model the answer is 'basically never'! But we think violations should be small in practice and provide sensitivity analysis.
October 16, 2025 at 2:43 AM
Next, we ask 'when will the key assumptions be satisfied'? We argue that under a causal pie model the answer is 'basically never'! But we think violations should be small in practice and provide sensitivity analysis.
Then we develop Neyman orthogonal estimators for when S is only independent of Y(1) given Y(0) and covariates.
October 16, 2025 at 2:43 AM
Then we develop Neyman orthogonal estimators for when S is only independent of Y(1) given Y(0) and covariates.
First, we simply point out that you don't really need multiple studies. You just need one study and a baseline covariate S that you can use to make your own 'substudies' that satisfy their assumptions. This gives you more control over whether the assumptions are approximately satisfied.
October 16, 2025 at 2:43 AM
First, we simply point out that you don't really need multiple studies. You just need one study and a baseline covariate S that you can use to make your own 'substudies' that satisfy their assumptions. This gives you more control over whether the assumptions are approximately satisfied.
Dare I say that this post is ironically on the verge of starting a lively debate?
September 5, 2025 at 9:58 PM
Dare I say that this post is ironically on the verge of starting a lively debate?
Ha, yeah, that was "unconstructive".
Guess you can usually tell who wants feedback and who's celebrating from the wording of their post. Definitely shouldn't rain on people's parade when they're clearly celebrating
Guess you can usually tell who wants feedback and who's celebrating from the wording of their post. Definitely shouldn't rain on people's parade when they're clearly celebrating
August 7, 2025 at 4:52 PM
Ha, yeah, that was "unconstructive".
Guess you can usually tell who wants feedback and who's celebrating from the wording of their post. Definitely shouldn't rain on people's parade when they're clearly celebrating
Guess you can usually tell who wants feedback and who's celebrating from the wording of their post. Definitely shouldn't rain on people's parade when they're clearly celebrating
I'm sure this a subtweet of something that I wouldn't defend, but I do post papers here in large part to hopefully get feedback...
August 7, 2025 at 4:26 PM
I'm sure this a subtweet of something that I wouldn't defend, but I do post papers here in large part to hopefully get feedback...
Basic idea is: you want to know the effect of some exposure on longevity (via MR). You don't know how long the people in your sample will live. But you know how long their parents lived. So you use sample's genes as IVs, sample's exposure, parents' lifespan as outcomes. When/why would this work?
August 6, 2025 at 5:26 PM
Basic idea is: you want to know the effect of some exposure on longevity (via MR). You don't know how long the people in your sample will live. But you know how long their parents lived. So you use sample's genes as IVs, sample's exposure, parents' lifespan as outcomes. When/why would this work?
Does this paper (pubmed.ncbi.nlm.nih.gov/34669630/) address the issues raised in that paper?
Constructed Measures and Causal Inference: Towards a New Model of Measurement for Psychosocial Constructs - PubMed
Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurem...
pubmed.ncbi.nlm.nih.gov
August 6, 2025 at 2:52 PM
Does this paper (pubmed.ncbi.nlm.nih.gov/34669630/) address the issues raised in that paper?
7 year old son and 3 year old daughter trade Beatles and frozen songs on car rides and he always chooses this one because it’s their longest. I’ve developed quite an appreciation for it
July 5, 2025 at 5:43 PM
7 year old son and 3 year old daughter trade Beatles and frozen songs on car rides and he always chooses this one because it’s their longest. I’ve developed quite an appreciation for it