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We propose a method for causal inference using satellite image time series,in order to determine the treatment effects of interventions which impactclimate 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 andforest fires. As a concrete example, we focus on quantifying forest tree coverchange/ deforestation due to human led causes. The proposed method involves thefollowing steps. First, we uae computer vision and machine learning/deeplearning techniques to detect and quantify forest tree coverage levels overtime, at every time epoch. We then look at this time series to identifychangepoints. Next, we estimate the expected (forest tree cover) values using aBayesian structural causal model and projecting/forecasting the counterfactual.This is compared to the values actually observed post intervention, and thedifference in the two values gives us the effect of the intervention (ascompared to the non intervention scenario, i.e. what would have possiblyhappened without the intervention). As a specific use case, we analyzedeforestation levels before and after the hyperinflation event (intervention)in Brazil (which ended in 1993-94), for the Amazon rainforest region, aroundRondonia, Brazil. For this deforestation use case, using our causal inferenceframework can help causally attribute change/reduction in forest tree cover andincreasing deforestation rates due to human activities at various points intime.

Causal inference for climate change events from satellite image time series using computer vision and deep learning