Causal Inference with Unobserved Confounding: Leveraging Negative Control Outcomes Using Lavaan

Causal conclusions about non-randomized treatments rest on the absence of unobserved confounding. While often made in practice, this fundamental yet empirically untestable assumption can rarely be definitively justified. In most realistic settings, the threat of unobserved confounding lurks. Can causal effects be unbiasedly estimated when unobserved confounding is present? In this tutorial, we introduce an approach from the causal inference and epidemiological literature that permits doing so: negative control outcomes. We explain what a negative control outcome is and how to leverage it to counteract bias due to unobserved confounding. Estimation using a negative control outcome is carried out using the Control Outcome Calibration Approach (COCA). To encourage the adoption of COCA in practice, we implement COCA using lavaan, a popular and free statistical modeling software in R. We illustrate COCA using two publicly available real-world datasets. COCA is practically elegant, straightforward to implement, and under certain assumptions about the potential outcomes, able to unbiasedly estimate causal effects even when unobserved confounding is present.