Christian Tien

Christian Tien

PhD Economics

University of Cambridge

Biography

Christian Tien is a final year PhD student in Economics at the University of Cambridge. His research interests include causal inference and machine learning. He likes to come up with creative approaches to interesting economic identification problems. His research in the intersection of instrumental variables and proximal learning received the Tom Ten Have Award honorable mention at the Americal Causal Inference Conference 2023.

Interests
  • Causal Inference
  • Machine Learning
Education
  • PhD in Economics, 2023

    University of Cambridge

  • MPhil in Economic Research, 2019

    University of Cambridge

  • BA in Economics, 2018

    University of Cambridge

Papers

Relaxing Instrument Exclusion with Common Confounders
Relaxing Instrument Exclusion with Common Confounders

Finding excluded (and exogenous) instruments is hard. We consider the situation where instruments are excluded only conditional on some unobserved common confounders, for which relevant proxies exist. Using insights from proximal learning, we can identify exogenous variation in the instruments to then identify a causal effect of a treament on an outcome. All our relevance assumptions are testable, while as usual in IV, the assumption of exclusion conditional on unobservables is not (up to specification tests). Importantly, exclusion conditional on unobservables for which proxies exist may be a weaker assumption than exclusion conditional on observables only.

Selected Talks

American Causal Inference Conference 2023
Received Tom Ten Have Award honorable mention for poster presentation of Relaxing Instrument Exclusion with Common Confounders.
American Causal Inference Conference 2023
EEA-ESEM 2022
Presented Instrumented Common Confounding in the session Identification of Treatment Effects.
EEA-ESEM 2022
American Causal Inference Conference 2022
Presented Instrumented Common Confounding for the first time as a poster.
American Causal Inference Conference 2022

Skills

Python
Statistics
R

Contact