**Richard Guo**

#### Statistical Laboratory, DPMMS, University of Cambridge.

I am currently a research fellow with the *Causality* program at the Simons Institute, UC Berkeley. I am co-organizing a reading group on *algebraic aspects of graphical models*.

I am also a Research Associate at the Statistical Laboratory of University of Cambridge, mentored by Prof. Rajen Shah.

I recently completed my PhD in Statistics from University of Washington, Seattle. I work on problems related to causal inference. My PhD advisor is Thomas S. Richardson. My doctoral thesis is on *Likelihood analysis of causal models*.

I work on statistical foundations of causal inference, including model selection, irregularity and partial identifiability. Recently, I have been working on the efficiency theory of causal graphical models.

## news

May 20, 2022 | Attending ACIC 2022 and presenting in Poster Session #1 (May 24 @5pm). |

Feb 24, 2022 | What is the most “efficient” identifying formula for your causal effect? This new preprint answers this question in the context of causal DAGs. |

Feb 5, 2022 | Try simplifying your causal DAG with reduceDAG? |

Sep 30, 2021 | I will be the Richard M. Karp Research Fellow for the Causality program at the Simons Institute Jan–May, 2022. |