 Though promising, criticisms have been raised against Steel’s mechanistic approach to extrapolation. One worry, raised by Jeremy Howick et al. (2013), is that in order to identify the bottlenecks and downstream differences, we must know more about the target than Steel admits. If what we know about the mechanism in the target exceeds what we know about the mechanism in the model, the extrapolator’s circle will not have been avoided. Another worry with Steel’s approach to extrapolation is that it doesn’t avoid the masking problem. According to Julian Reiss (2010), Federica Russo (2010), and Brendan Clarke et al. (2014), even if we establish that X causes Y through some mechanism, this doesn’t seem to eliminate the possibility of there being several paths that link X to Y. For example, there may be an upstream difference that affects the outcome but does not pass through the downstream stages of the mechanism. (This problem is taken up again below in  Section 3.4.)  A third worry with Steel’s approach, which comes from Tudor Baetu (2016), is that mechanistic accounts of the experimental model of interest “combine data from tens or even hundreds of distinct experimental setups” (956). The resulting big picture account of the experimental model is an aggregate of findings that do not describe a mechanism that actually exists in any cell or organism. Instead, as a number of authors have also pointed out (Huber and Keuck 2013; Lemoine 2017; Nelson 2013), the mechanism of interest is often stipulated first and then verified piecemeal in many different experimental organisms. The result is what Mael Lemoine (2017) has called a “theoretical chimera”, a hypothesis supported by heterogeneous partial models. On the chimera view of extrapolation, it’s possible that all one-to-one analogies work, and yet the aggregate theoretical chimera model fails. 