Causal structure learning over time: Observations and interventions
Seven studies examined how people learn causal relationships in scenarios when the
variables are temporally dependent–the states of variables are stable over time. When …
variables are temporally dependent–the states of variables are stable over time. When …
Causal strength induction from time series data.
One challenge when inferring the strength of cause-effect relations from time series data is
that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted …
that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted …
Naïve theories of causal force and compression of elapsed time judgments.
D Faro, AL McGill, R Hastie - Journal of personality and social …, 2010 - psycnet.apa.org
Recent research has shown that when people perceive a causal relation between 2 events,
they “compress” the intervening elapsed time. The present work shows that a naïve …
they “compress” the intervening elapsed time. The present work shows that a naïve …
Searching for the best cause: Roles of mechanism beliefs, autocorrelation, and exploitation.
BM Rottman - … of Experimental Psychology: Learning, Memory, and …, 2016 - psycnet.apa.org
When testing which of multiple causes (eg, medicines) works best, the testing sequence has
important implications for the validity of the final judgment. Trying each cause for a period of …
important implications for the validity of the final judgment. Trying each cause for a period of …
The acquisition and use of causal structure knowledge
BM Rottman - The Oxford handbook of causal reasoning, 2017 - books.google.com
This chapter provides an introduction to how humans learn and reason about multiple
causal relations connected together in a causal structure. The first half of the chapter focuses …
causal relations connected together in a causal structure. The first half of the chapter focuses …
[PDF][PDF] Elements of a rational framework for continuous-time causal induction
Temporal information plays a major role in human causal inference. We present a rational
framework for causal induction from events that take place in continuous time. We define a …
framework for causal induction from events that take place in continuous time. We define a …
Learning about causal relations that change over time: primacy and recency over long timeframes in causal judgments and memory
Being able to notice that a cause–effect relation is getting stronger or weaker is important for
adapting to one's environment and deciding how to use the cause in the future. We …
adapting to one's environment and deciding how to use the cause in the future. We …
When and how do people reason about unobserved causes
Assumptions and beliefs about unobserved causes are critical for inferring causal
relationships from observed correlations. For example, an unobserved factor can influence …
relationships from observed correlations. For example, an unobserved factor can influence …
Effect of grou** of evidence types on learning about interactions between observed and unobserved causes.
BM Rottman, W Ahn - Journal of Experimental Psychology …, 2011 - psycnet.apa.org
When a cause interacts with unobserved factors to produce an effect, the contingency
between the observed cause and effect cannot be taken at face value to infer causality. Yet it …
between the observed cause and effect cannot be taken at face value to infer causality. Yet it …
Causal learning with interrupted time series data
People often test changes to see if the change is producing the desired result (eg, does
taking an antidepressant improve my mood, or does kee** to a consistent schedule …
taking an antidepressant improve my mood, or does kee** to a consistent schedule …