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Vivian Zheng
Vivian Zheng

277 Followers

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Published in

Towards Data Science

·Sep 5, 2018

Is Your Standard Error Robust?

Practical Guide to Picking the Correct Specification Executive Summary Problem: Default standard errors (SE) reported by Stata, R and Python are right only under very limited circumstances. Specifically, these programs assume that your regression error is independently and identically distributed. …

Data Science

6 min read

Is Your Standard Error Robust?
Is Your Standard Error Robust?
Data Science

6 min read


Published in

Towards Data Science

·Jul 24, 2018

Frontiers of Recommendation Systems & Diversity

Critical Reading: Papers from 2011 ACM RecSys and Management Science Introduction Recently, I’ve been trying to understand how recommendation systems affects the diversity of user experience. Before I start my own project, I’d like to see what smart people have done. So I read the entire collection of papers from ACM…

Data Science

14 min read

Frontiers of Recommendation Systems & Diversity
Frontiers of Recommendation Systems & Diversity
Data Science

14 min read


Published in

Towards Data Science

·Jul 17, 2018

The Making of Great Hypothesis

How Bill and Melinda Gates’ favorite book Factfulness will leapfrog your data science practice Motivation So far, data science education focus heavily on data wrangling, hypothesis testing and causal inference. Very rarely do we hear about the preceding step — hypothesis formation. Forming the wrong hypothesis is costly Once you’re set on testing a hypothesis, you can…

Data Science

8 min read

The Making of Great Hypothesis
The Making of Great Hypothesis
Data Science

8 min read


Published in

Towards Data Science

·Jun 19, 2018

The Holy Grail of Causal Inference

What is Structural Estimation? Introduction Structural estimation, the holy grail of applied econometrics, the two words that drive fear into the bravest souls. Nail it as a PhD student, and you’re on your way to a top tenure-track professorship. So what is the magic? Specifically, how can it outperform the usual…

Data Science

6 min read

The Holy Grail of Causal Inference
The Holy Grail of Causal Inference
Data Science

6 min read


Published in

Towards Data Science

·May 24, 2018

Why is academic writing so dense?

I analyzed 100K papers to find out. Motivation Recently, I took a long break from posting on Medium. The 2nd half of the Public Finance class at Stanford is about structural estimation. These paper easily run up to 100 pages/paper, and are filled with greek letters and lengthy, complex sentences. This…

Data Science

7 min read

Why is academic writing so dense?
Why is academic writing so dense?
Data Science

7 min read


Published in

Towards Data Science

·Apr 24, 2018

Causal inference 101: difference-in-differences

Case study: who pays for mandated childbirth coverage? In today’s Public Finance III lecture @ Stanford, Professor Petra introduces one of the most widely used causal inference technique: difference-in-differences (diff-in-diff). To make our discussion less dry, she motivates the need for this cool technique in the context of mandated benefits. The Question …

Economics

5 min read

Causal Inference 101: Difference-in-differences
Causal Inference 101: Difference-in-differences
Economics

5 min read


Published in

Towards Data Science

·Apr 15, 2018

The Danger of Eyeball Data Science

Does unemployment benefit make people lazy? — This week in Public Finance III @ Stanford University, Professor Persson talks about optimal insurance design. This class has 78 lecture slides in total. I will compile them into a breezy, 7-minute read for you. The Danger of Eyeball Data Science Does unemployment benefit make people lazy? If it does, states with higher benefit would have…

Economics

7 min read

The Danger of Eyeball Data Science
The Danger of Eyeball Data Science
Economics

7 min read


Apr 9, 2018

Lecture 2: Public Finance III @ Stanford University

In this lecture, Professor Perrson tackles the problem of asymmetric information. We will see two Nobel-winning models, and discuss ways to detect adverse selection. This lecture has 55 slides, and I will condense it into a 7 min read for you. Introduction Imagine you’re running an insurance company. How would you turn a profit? As in any other business, you have to price…

Insurance

7 min read

Lecture 2: Public Finance III @ Stanford University
Lecture 2: Public Finance III @ Stanford University
Insurance

7 min read

Vivian Zheng

Vivian Zheng

277 Followers

Stanford Economics PhD

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