Two Layers of Instability in Causal Estimation
There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed. In particular, Robins and Ritov (1997) and Robins et al. (2003) showed that causal effects can be discontinuous as a function of the data distribution: two arbitrarily close data distributions might correspond to different causal effects. This is a fact independent of the choice of estimator; however, not all estimators are equally unstable. Our contribution is to surface a second layer of instability that depends on the choice of estimator. We show that many standard point estimates can be read as point summaries of multimodal distributions over the space of ...
| Authors | Alexis Bellot |
|---|---|
| Primary category | stat.ML |
| Published | 19 June 2026 |
| Last updated at source | 5 July 2026 |
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