Research & Analysis Workflows

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?

Raster geospatial data processing — the Python interface to GDAL for satellite imagery, elevation models, and grid-based geographic analysis. Rasterio reads and writes georeferenced raster formats (GeoTIFF, NetCDF, JP2, PNG, JPEG2000), handles Coordinate Reference Systems (CRS) and reprojection, performs band math (NDVI, NDWI, EVI), clips/masks rasters with vector geometries, resamples grids, and supports memory-efficient windowed I/O for multi-gigabyte files. Use when: working with satellite imagery or aerial photos, processing Digital Elevation Models (DEM/DTM/DSM), computing spectral indices from multispectral data, clipping raster data to polygon boundaries, reprojecting between coordinate systems, performing spatial interpolation on gridded data, analyzing land cover or land use change over time, integrating raster data with vector data (geopandas/shapely), or any task involving georeferenced grid/pixel data as opposed to vector points/lines/polygons.

Natural Language Processing for text analysis, corpus linguistics, and production NLP pipelines. spaCy provides fast production-grade tokenization, POS tagging, NER, dependency parsing, and custom model training. NLTK provides classical corpus linguistics, linguistic analysis, VADER sentiment, collocation analysis, and access to standard linguistic corpora. Use when: processing and analyzing text data, extracting named entities (people, orgs, locations, dates), dependency parsing and syntactic analysis, building text classification pipelines, performing corpus-level linguistic analysis (frequency, collocations, readability), sentiment analysis, lemmatization and stemming, working with multilingual text, training custom NER or text classifiers, or any task requiring structured understanding of natural language beyond simple string operations.