Causal Inference Symposium: From Computer Science to Neuroscience

Causal Inference Symposium: From Computer Science to Neuroscience

08:55 AM ? 09:00 Alcino Silva (UCLA): Introduction

09:00 AM ? 09:18 Chris Hitchcock (Caltech): Causation in science and everyday life

09:18 AM ? 09:23 Q & A

09:23 AM ? 09:41 Frederick Eberhardt (Caltech): Large and small causes

09:41 AM ? 09:46 Q & A

09:46 AM ? 10:04 Ralph Adolphs (Caltech): What causes our thoughts and feelings?

10:04 AM ? 10:09 Q& A

10:09 AM ? 10:27 Pietro Perona (Caltech): Causality in vision

10:27 AM ? 10:32 Q & A

10:32 AM ?10:52 BREAK

10:52 AM ? 11:10 Alcino Silva (UCLA): Research maps: an approach to integrate and gauge causal information

11:10 AM ? 11:15 Q & A

11:15 AM ? 11:33 William Hsu (UCLA): Causal inference in experiment planning

11:33 AM ? 11:38 Q & A

11:38 AM ? 11:53 Onyebuchi Arah (UCLA): Regression diagrams in causal analysis

11:53 AM ? 11:58 Q & A

11:58 AM ? 12:16 Chad Hazlett (UCLA): Making sense of sensitivity: Extending omitted variable bias

12:16 PM ? 12:21 Q & A
n12:21 PM ? 1:21 LUNCH

1:21 PM ? 1:39 Sander Greenland (UCLA): Cognitive distortions of causal-inference statistics: Why
observational studies ofstatistical stupidity should inform analysis methodology

1:39 PM ? 1:44Q & A

1:44 PM ? 2:02 Maya Petersen (Berkeley): Integrating machine learning in causal effect estimation

2:02 PM ? 2:20 Q & A

2:20 PM ? 2:38 Patricia Cheng (UCLA): Is natural human learning rational?

2:38 PM ? 2:43 Q & A

2:43 PM ? 3:01 Aaron Blaisdell (UCLA): Causal reasoning in rodents

3:01 PM ? 3:06 Q & A

3:06 PM ? 3:26 BREAK

3:26 PM ? 3:44 MayankMehta (UCLA): Neuronal dynamics of predictive coding

3:44 PM ? 3:49 Q &A

3:49 PM ? 4:07 Huiying Li (UCLA): The human skin microbiome in healthand disease

4:07 PM ? 4:12 Q & A

4:12 PM ? 4:30 Jonathan Braun (UCLA): Illness as metaphor ? what the microbiome teaches us about the nature ofhealth

4:30 PM ? 4:35 Q & A

4:35 PM ? 4:53 Judea Pearl (UCLA): The Causal Revolution, the ladder of causation, machine learning, and strong AI.

4:53 PM ? 4:58 Q & A

4:58 PM DISCUSSION

5:15 PM RECEPTION