Taro Fujita, Center for Doctoral Studies in Economics (CDSE): Causal Inference und Machine Learning (Februar 2024)

Taro Fujita ist Doktorand im Fach VWL an der Universität Mannheim. Er interessiert sich für Ökonometrie, insbesondere an der Schnittstelle von kausaler Inferenz und maschinellem Lernen. Vor seinem Studium in Mannheim arbeitete er als Finanz­analyst bei der Bank of Japan.

What is your current research topic?

I am interested in econometrics, a field that applies statistical methods to the analysis of economic data. A key focus in econometrics is causal inference, which seeks to understand the cause-and-effect relations­hip between economic variables, such as how education influences future income or the effects of job-training programs on employment. My interest lies in applying modern machine learning tools to develop more robust and flexible econometric methods.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

Do you think going to a better school makes you richer? I don’t know the answer, but let’s see what the data suggests.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

understanding the reality

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

Application of modern machine learning tools in econometrics is growing rapidly.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?


What development opportunities do you see for the topic of Data Science in relation to your field?

Application of machine learning tools (but in a more transparent way).