I am a cardiovascular epidemiologist. As your article mentions, predicting/causal inference is a question I am frequently asked, and it is indeed a challenging one to articulate. One perspective I have been taught is that observational studies are not sufficient for drawing causal inferences, which is why ambiguous terms (such as risk factors or associations) are used (as you rightly point out, this should be avoided, and I fully agree). However, I am perplexed about how to choose terms after avoiding them. My understanding is that a reasonable causal inference requires the integration of many studies, including observational research, and information beyond the data. However, this seems to contradict what you are saying. I find both perspectives reasonable. Causal inference is complex, and honestly, I still do not understand how to conduct a perfect causal inference. The more literature I read, the more confused I become about what I am actually doing. Causal inference requires assessing the impact of interventions (is that right?), and it is also challenging in observational studies. Now I wonder if my analytical methods are problematic. I lack a reasonable analysis plan and methodology. If possible, could you provide me with some reference papers on epidemiological (observational study) practices?
Thank you, this is really helpful. I worked in the humanities as a methodologist and, when asked to help, would always start with 'what is your question?'. Very often and very telling the response was 'I want to show support for X". We would then have a 'but if you already know X is true, then what is the question you hope to answer with your research?" discussion. Research was too often seen as a form of advocacy. Focusing on the question was most useful to identify this misconception.
Excellent read, thank you. Having designed a few studies, in medicine and even in unrelated fields, I appreciate the rigor required to get it right. And, thanks for the references. I'm creating a short-course where we're trying to teach a little bit of epidemiology and I've now got more background reading for the class!
I am a cardiovascular epidemiologist. As your article mentions, predicting/causal inference is a question I am frequently asked, and it is indeed a challenging one to articulate. One perspective I have been taught is that observational studies are not sufficient for drawing causal inferences, which is why ambiguous terms (such as risk factors or associations) are used (as you rightly point out, this should be avoided, and I fully agree). However, I am perplexed about how to choose terms after avoiding them. My understanding is that a reasonable causal inference requires the integration of many studies, including observational research, and information beyond the data. However, this seems to contradict what you are saying. I find both perspectives reasonable. Causal inference is complex, and honestly, I still do not understand how to conduct a perfect causal inference. The more literature I read, the more confused I become about what I am actually doing. Causal inference requires assessing the impact of interventions (is that right?), and it is also challenging in observational studies. Now I wonder if my analytical methods are problematic. I lack a reasonable analysis plan and methodology. If possible, could you provide me with some reference papers on epidemiological (observational study) practices?
Thank you, this is really helpful. I worked in the humanities as a methodologist and, when asked to help, would always start with 'what is your question?'. Very often and very telling the response was 'I want to show support for X". We would then have a 'but if you already know X is true, then what is the question you hope to answer with your research?" discussion. Research was too often seen as a form of advocacy. Focusing on the question was most useful to identify this misconception.
Excellent read, thank you. Having designed a few studies, in medicine and even in unrelated fields, I appreciate the rigor required to get it right. And, thanks for the references. I'm creating a short-course where we're trying to teach a little bit of epidemiology and I've now got more background reading for the class!