The topic of this lecture is causality – namely, our awareness of what causes what in the world and why it matters. Though it is basic to human thought, causality is a notion shrouded in mystery, controversy,
and caution, because scientists and philosophers have had difficulties defining when one event truly causes another. We all understand that the rooster’s crow does not cause the sun to rise, but even this
simple fact cannot easily be translated into a mathematical equation. Today, I would like to share with you a set of ideas which I have found very useful in studying phenomena of this kind. These ideas have led to practical tools that I hope you will find useful on your next encounter with cause and effect. It is hard to imagine anyone here who is not dealing with cause and effect. Whether you are evaluating the impact of bilingual education programs or running an experiment on how mice distinguish food from danger or speculating about why Julius Caesar crossed the Rubicon or diagnosing a patient or predicting who will win the
presidential election, you are dealing with a tangled web of cause–effect considerations. The story that I am about to tell is aimed at helping researchers deal with the complexities of such considerations, and to clarify their meaning. This lecture is divided into three parts. I begin with a brief historical sketch of the difficulties that various disciplines have had with causation. Next I outline the ideas that reduce or eliminate several of these historical difficulties. Finally, in honor of my engineering background, I will show how these ideas lead to simple practical tools, which will be demonstrated in the areas of statistics and social science.
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and caution, because scientists and philosophers have had difficulties defining when one event truly causes another. We all understand that the rooster’s crow does not cause the sun to rise, but even this
simple fact cannot easily be translated into a mathematical equation. Today, I would like to share with you a set of ideas which I have found very useful in studying phenomena of this kind. These ideas have led to practical tools that I hope you will find useful on your next encounter with cause and effect. It is hard to imagine anyone here who is not dealing with cause and effect. Whether you are evaluating the impact of bilingual education programs or running an experiment on how mice distinguish food from danger or speculating about why Julius Caesar crossed the Rubicon or diagnosing a patient or predicting who will win the
presidential election, you are dealing with a tangled web of cause–effect considerations. The story that I am about to tell is aimed at helping researchers deal with the complexities of such considerations, and to clarify their meaning. This lecture is divided into three parts. I begin with a brief historical sketch of the difficulties that various disciplines have had with causation. Next I outline the ideas that reduce or eliminate several of these historical difficulties. Finally, in honor of my engineering background, I will show how these ideas lead to simple practical tools, which will be demonstrated in the areas of statistics and social science.
You should get some sleep if you’re tired
I get paid tomorrow also I cant wait for that money lol
Fresh start
How was your day? Lol
If it went good or not
Also don’t be sad and don’t beat me with gifts
Cause now I’m sad since I’m broke as s**t still..
Well I hope all gets fixed between the two of you
It’s not because I don’t want to talk it’s because I forget
And that’s true I only will look like a slob around my best friend
But that’s because I’ve known her for most of my life