@congjiang08.bsky.social
Reposted
Totally agree with this, and would double down: description of causal mechanisms is the foundation of science. If we can’t describe causal mechanisms, no interventions can follow.
Don't let the causal inference buzz fool you: Description is the foundation of science.
We've described the 3-year health impact of #COVID19 in Madrid, the EU region with the highest life expectancy.
If we can't describe, no causal inference can follow.
academic.oup.com/ofid/article...
We've described the 3-year health impact of #COVID19 in Madrid, the EU region with the highest life expectancy.
If we can't describe, no causal inference can follow.
academic.oup.com/ofid/article...
Three Years of the Coronavirus Disease 2019 Pandemic in a European Region: A Population-Based Longitudinal Assessment in Madrid Between 2020 and 2022
Using population-based data from several linked administrative and clinical databases, we characterized the health burden of COVID-19 longitudinally in the near
academic.oup.com
December 23, 2024 at 10:46 AM
Totally agree with this, and would double down: description of causal mechanisms is the foundation of science. If we can’t describe causal mechanisms, no interventions can follow.
Reposted
1/
If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
February 3, 2025 at 2:51 PM
1/
If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
Reposted
💡A new paper by Elias Bareinboim and Drago Plecko underscores the intractability of ignorability assumptions commonly invoked in the potential outcomes framework, explains why structural causal models—explicitly grounded in well-defined causal mechanisms—are far easier to interpret. 1/2
August 24, 2025 at 1:19 PM
💡A new paper by Elias Bareinboim and Drago Plecko underscores the intractability of ignorability assumptions commonly invoked in the potential outcomes framework, explains why structural causal models—explicitly grounded in well-defined causal mechanisms—are far easier to interpret. 1/2