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DTSTAMP:20250731T050300Z
DESCRIPTION:Title: Deep down\, everyone wants to be causal/\n\nRESUME / ABS
 TRACT :\n	Most researchers in the social\, behavioral\, and health sciences
  are taught to be extremely cautious in making causal claims. However\, ca
 usal inference is a necessary goal in research for addressing many of the 
 most pressing questions around policy and practice. In the past decade\, c
 ausal methodologists have increasingly been using and touting the benefits
  of more complicated machine learning algorithms to estimate causal effect
 s. These methods can take some of the guesswork out of analyses\, decrease
  the opportunity for “p-hacking\,” and may be better suited for more fine-
 tuned tasks such as identifying varying treatment effects and generalizing
  results from one population to another. However\, should these more advan
 ced methods change our fundamental views about how difficult it is to infe
 r causality? In this talk I will discuss some potential advantages and dis
 advantages of using machine learning for causal inference and emphasize wa
 ys that we can all be more transparent in our inferences and honest about 
 their limitations.\n\nZoom link for this lecture only\n\nJoin Zoom Meeting
  https://mcgill.zoom.us/j/9791073141\n\nMeeting ID: 979 107 3141\n\n \n\n 
 \n
DTSTART:20210924T190000Z
DTEND:20210924T200000Z
SUMMARY:Jennifer Hill (NYU Steinhardt)
URL:/mathstat/channels/event/jennifer-hill-nyu-steinha
 rdt-333644
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