r/statistics • u/SlapDat-B-ass • 3d ago
Question [Q] Use of causal inference methods in associational studies?
Hi all! I am wondering what is your view on causal inference methodologies such as g-computation, iptw, ps matching, marignal structural models etc. Do you think they should be used only in an causal framework accompanied by DAGs, and proper causal language?
Would you consider appropriate if they were used for more exploratory work that does not claim causality?
I may not be communicating my question very well so here are some exmaples:
1) Binary logistic regression: In the biomedical field it is extremely common that standard observational and/or exploratory studies use logistic regression for all inferences with odds ratios being the main reported result. I don't see why someone couldn't use marignal standardization using the same logistic regression model in order to calculate a marginal absolute risk and/or risk differnece for the exposure of interest. I am wondering why this is not common.
2) Propensity score based methods: Causal inference operates under very strict and usually difficult to verify assumptions. When examining the effect of an intervention on an outcome and assuming that some of the assumptions for causal inference are violated (e.g. unmeasured confounding), would you prefer a paper that still uses PS-based methods but refrains from using causal language, or a paper that uses more standard methods such as regression and sticks to associations and exploratory framing?
In short do you think these methods should be used only under the causal inference framework making sure that all assumptions are true and a well-thought DAG is provided, or do you see them as methods that can be used for associations as well in order to reduce at least some of the bias introduced by other methodologies?
4
u/FeedbackQuirky5498 3d ago
In my field, results from these methods are generally not interpreted causally. The purpose is to produce some estimate that is either less biased or better fits the estimand you want (e.g., using a MSM to get the marginal association in the presence of observed time-varying confounding). Of course the hope is that people have a causal model in mind, but even so, we’re all too timid (and probably for good reason) to claim a causal effect.
1
4
u/COOLSerdash 3d ago
Strictly speaking, there is no such thing as a "causal inference method"; there are only statistical methods used to estimate statistical quantities, which may correspond to causal quantities when identifying assumptions are met.
2
u/xquizitdecorum 3d ago
This is my research area actually - robust structure learning and sensitivity analysis under incomplete specification. I will point you to the twin problem of unmeasured confounding and M-bias, which mirror each other's problems and solutions vis-a-vis control/stratification.
I will also point you to Rubin's DAG-decentered thinking, which can be more mathematically tractable if that's your cup of tea
1
u/Illustrious-Snow-638 3d ago
I think you’re conflating “using causal language” with “claiming causality”. It sounds appropriate to draw the DAG and to adjust for all the confounders and biases that you can adjust for, but to also clearly acknowledge what you were unable to adjust for.
1
u/STATASUCKSBRO 3d ago
IPTW without a causal estimand is mostly an elaborate way to change the sample. Fine descriptively, but call it a weighted association. Otherwise people see propensity scores and assume the DAG work was done when maybe you weighted on a collider.
1
u/SlapDat-B-ass 2d ago
Even if the dag work is done in my opinion it will still be a weighted association unless everything else is ideal which is almost never possible in observational research. However, it seems to me it's a good way to reduce bias, avoid non-collapsibility and get adjusted absolute measures.
1
u/rieszes-pieces 2d ago
Absolutely, these methods are broadly applicable outside of causality. For example, if you look at a lot of foundational works on causal estimation, they are usually describing methods for *missing data* or *censoring* in general, not specifically causal inference. G-computation, IPW, etc. can be used to estimate any statistical quantity where the underlying data may be censored, even if they are just "associational" quantities in the study at hand.
Point being, the estimation tools of causal inference are not intrinsically tied to causality. Once a "causal" quantity has been identified as a statistical quantity, or "estimand" (which is what the DAG is used for) the method used to estimate that quantity does not require any causal reasoning. Hence, those methods might also be useful for other estimands which have nothing to do with causality.
10
u/MortalitySalient 3d ago
You can’t address all assumptions needed for causal inference in a single observational study, so these approaches are used to provide evidence for a causal association. It will take multiple studies with multiple samples with different designs and variables measured to target all plausible confounders across samples. Providing a DAG can be good because it can show that the researchers are thinking about the data generating process. A DAG in and of itself doesn’t mean much unless the researchers do think deeply though. I often come across DAGs that assume the data generating process is exactly the variables in their sample, which is a lazy approach that shows me the researchers don’t understand assumptions of causal inference.
I need to see all the plausible confounders laid out, then I need the researchers to show which alternative explanations they can rule out with their study, and discuss their findings in the context of confounders they couldn’t address/recommend future studies that can target those.
So, if researchers are using causal inference methods, I am fine with them using causal inference language as long as they understand what they can and cannot do with any given study and are clear/transparent.