  We propose a method for causal inference using satellite image time series,
in order to determine the treatment effects of interventions which impact
climate change, such as deforestation. Simply put, the aim is to quantify the
'before versus after' effect of climate related human driven interventions,
such as urbanization; as well as natural disasters, such as hurricanes and
forest fires. As a concrete example, we focus on quantifying forest tree cover
change/ deforestation due to human led causes. The proposed method involves the
following steps. First, we uae computer vision and machine learning/deep
learning techniques to detect and quantify forest tree coverage levels over
time, at every time epoch. We then look at this time series to identify
changepoints. Next, we estimate the expected (forest tree cover) values using a
Bayesian structural causal model and projecting/forecasting the counterfactual.
This is compared to the values actually observed post intervention, and the
difference in the two values gives us the effect of the intervention (as
compared to the non intervention scenario, i.e. what would have possibly
happened without the intervention). As a specific use case, we analyze
deforestation levels before and after the hyperinflation event (intervention)
in Brazil (which ended in 1993-94), for the Amazon rainforest region, around
Rondonia, Brazil. For this deforestation use case, using our causal inference
framework can help causally attribute change/reduction in forest tree cover and
increasing deforestation rates due to human activities at various points in
time.
