You are all cordially invited to the AMLab seminar on **Tuesday April 4 at 16:00 in C3.163**, where **Tineke Blom** will give a talk titled “**Causal Discovery in the Presence of Measurement Error**”. Afterwards there are the usual drinks and snacks!

**Abstract**: Causal discovery algorithms can predict causal relationships based on several assumptions, which include the absence of measurement error. However, this assumption is most likely violated in practical applications, resulting in erroneous, irreproducible results. In this work, we examine the effect of different types of measurement error in a linear model of three variables, which is a minimal example of an identifiable causal relationship. We show that the presence of unknown measurement error makes it impossible to detect independences between the actual variables from the data using regular statistical testing and conventional thresholds for (in)dependence. We show that for limited measurement error, we can obtain consistent causal predictions by allowing for a small amount of dependence between (conditionally) independent variables. We illustrate our results in both simulated and real world protein-signaling data.