  A key challenge for robotic systems is to figure out the behavior of another
agent. The capability to draw correct inferences is crucial to derive human
behavior from examples.
  Processing correct inferences is especially challenging when (confounding)
factors are not controlled experimentally (observational evidence). For this
reason, robots that rely on inferences that are correlational risk a biased
interpretation of the evidence.
  We propose equipping robots with the necessary tools to conduct observational
studies on people. Specifically, we propose and explore the feasibility of
structural causal models with non-parametric estimators to derive empirical
estimates on hand behavior in the context of object manipulation in a virtual
kitchen scenario. In particular, we focus on inferences under (the weaker)
conditions of partial confounding (the model covering only some factors) and
confront estimators with hundreds of samples instead of the typical order of
thousands. Studying these conditions explores the boundaries of the approach
and its viability.
  Despite the challenging conditions, the estimates inferred from the
validation data are correct. Moreover, these estimates are stable against three
refutation strategies where four estimators are in agreement. Furthermore, the
causal quantity for two individuals reveals the sensibility of the approach to
detect positive and negative effects.
  The validity, stability and explainability of the approach are encouraging
and serve as the foundation for further research.
