  Crowd movement guidance has been a fascinating problem in various fields,
such as easing traffic congestion in unusual events and evacuating people from
an emergency-affected area. To grab the reins of crowds, there has been
considerable demand for a decision support system that can answer a typical
question: ``what will be the outcomes of each of the possible options in the
current situation. In this paper, we consider the problem of estimating the
effects of crowd movement guidance from past data. To cope with limited amount
of available data biased by past decision-makers, we leverage two recent
techniques in deep representation learning for spatial data analysis and causal
inference. We use a spatial convolutional operator to extract effective spatial
features of crowds from a small amount of data and use balanced representation
learning based on the integral probability metrics to mitigate the selection
bias and missing counterfactual outcomes. To evaluate the performance on
estimating the treatment effects of possible guidance, we use a multi-agent
simulator to generate realistic data on evacuation scenarios in a crowded
theater, since there are no available datasets recording outcomes of all
possible crowd movement guidance. The results of three experiments demonstrate
that our proposed method reduces the estimation error by at most 56% from
state-of-the-art methods.
