喬舒亞·安格里斯特
因果關(guān)系分析方法
The most important skill for an empirical researcher is not math, but judgment.
實證研究者最重要的技能不是數(shù)學(xué)，而是判斷力。
Good econometrics is like good carpentry—it's all about using the right tool for the job.
好的計量經(jīng)濟學(xué)就像好的木工——關(guān)鍵在于為工作使用正確的工具。
The difference between correlation and causation is not just academic—it can have profound policy implications.
相關(guān)性和因果關(guān)系之間的區(qū)別不僅僅是學(xué)術(shù)上的——它可能具有深遠(yuǎn)的政策影響。
Economics is at its best when it helps us solve real-world problems in a rigorous and transparent way.
當(dāng)經(jīng)濟學(xué)以嚴(yán)謹(jǐn)和透明的方式幫助我們解決現(xiàn)實世界的問題時，它處于最佳狀態(tài)。
Always question your assumptions—that's where the most interesting discoveries often lie.
永遠(yuǎn)質(zhì)疑你的假設(shè)——最有趣的發(fā)現(xiàn)往往就在那里。
The beauty of instrumental variables is that they allow us to see the world in a new light.
工具變量的美妙之處在于它們讓我們以新的視角看世界。
Causal inference is not just about statistical significance; it's about understanding the mechanisms behind the numbers.
因果推斷不僅僅是統(tǒng)計顯著性；它關(guān)乎理解數(shù)字背后的機制。
The best way to learn econometrics is by doing—working with real data and real problems.
學(xué)習(xí)計量經(jīng)濟學(xué)的最佳方法是通過實踐——處理真實的數(shù)據(jù)和真實的問題。
In economics, as in life, the answers are often more nuanced than they first appear.
在經(jīng)濟學(xué)中，就像在生活中一樣，答案往往比最初看起來更加微妙。
Education is one of the most powerful tools we have for reducing inequality and promoting social mobility.