Combining observations from multiple data sources creates a valuable resource for research. In causal inference, record linkage methods grant researchers access to the covariates needed for unbiased estimation and offer the means to explore (long-term) outcomes. This paper examines how record linkage conflicts with the assumptions required for identifying causal effects. Our investigation reveals that linkage errors primarily threaten consistency in the causal framework, leading to attenuation bias in the inference due to discrepancies in the linked data. In attempting to address this bias by being more stringent on the linkage, sampling bias inadvertently emerges from a covariate shift between the baseline and the linked samples, as profiles with more distinctive information are more likely to be recovered. Despite these challenges, we identify settings in which record linkage and causal inference can be soundly combined, and show how the effect estimated on linked data can be generalised, with linkage decisions informed accordingly. We further identify when existing and novel solutions support valid causal inference on linked data, and when inference should instead be treated with caution or even abandoned. We illustrate the challenges raised and the potential solutions using real data from a study of women's health and a simulation study.

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