Does Weather Matter? Causal Analysis of TV Logs
This addresses a gap in machine learning and data science by providing insights into human behavior for media and advertising industries, though it is incremental as it applies existing causal methods to a new domain.
The paper tackles the problem of understanding how weather affects TV watching patterns through causal analysis, showing that attributes like pressure and precipitation cause major changes in patterns, with this being the first large-scale study of its kind.
Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evidence and the abundance of data, weather has mostly been overlooked in the machine learning and data science research. This work presents a causal analysis of how weather affects TV watching patterns. We show that some weather attributes, such as pressure and precipitation, cause major changes in TV watching patterns. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.