Comparing Approaches to Causal Inference for Longitudinal Data: Inverse Probability Weighting versus Propensity Scores
Bibliographic record
Abstract
In observational studies for causal effects, treatments are assigned to experimental units without the benefits of randomization. As a result, there is the potential for bias in the estimation of the treatment effect. Two methods for estimating the causal effect consistently are Inverse Probability of Treatment Weighting (IPTW) and the Propensity Score (PS). We demonstrate that in many simple cases, the PS method routinely produces estimators with lower Mean-Square Error (MSE). In the longitudinal setting, estimation of the causal effect of a time-dependent exposure in the presence of time-dependent covariates that are themselves affected by previous treatment also requires adjustment approaches. We describe an alternative approach to the classical binary treatment propensity score termed the Generalized Propensity Score (GPS). Previously, the GPS has mainly been applied in a single interval setting; we use an extension of the GPS approach to the longitudinal setting. We compare the strengths and weaknesses of IPTW and GPS for causal inference in three simulation studies and two real data sets. Again, in simulation, the GPS appears to produce estimators with lower MSE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".