{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Forests\n", "\n", "https://github.com/grf-labs/grf/blob/master/experiments/acic18/script.R\n", "\n", "https://github.com/grf-labs/grf/tree/master/experiments/acic18" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:00:43.768770Z", "start_time": "2021-05-22T06:00:03.419Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "The downloaded binary packages are in\n", "\t/var/folders/8b/hhnbt0nd4zsg2qhxc28q23w80000gn/T//RtmpydKe1A/downloaded_packages\n" ] } ], "source": [ "install.packages('grf')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:01:28.307612Z", "start_time": "2021-05-22T06:01:27.452Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“package ‘grf’ was built under R version 3.6.2”" ] } ], "source": [ "library('grf')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:02:04.693441Z", "start_time": "2021-05-22T06:02:04.532Z" } }, "outputs": [], "source": [ "if(packageVersion(\"grf\") < '0.10.2') {\n", " warning(\"This script requires grf 0.10.2 or higher\")\n", "}\n", "library(sandwich)\n", "library(lmtest)\n", "library(Hmisc)\n", "library(ggplot2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:02:14.821484Z", "start_time": "2021-05-22T06:02:14.804Z" } }, "outputs": [], "source": [ "set.seed(1)\n", "\n", "rm(list = ls())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:02:31.172080Z", "start_time": "2021-05-22T06:02:31.058Z" } }, "outputs": [], "source": [ "data.all = read.csv(\"./data/synthetic_data.csv\")\n", "data.all$schoolid = factor(data.all$schoolid)\n", "\n", "DF = data.all[,-1]\n", "school.id = as.numeric(data.all$schoolid)\n", "\n", "school.mat = model.matrix(~ schoolid + 0, data = data.all)\n", "school.size = colSums(school.mat)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:02:42.334933Z", "start_time": "2021-05-22T06:02:41.793Z" } }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "glm(formula = Z ~ ., family = binomial, data = data.all[, -3])\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-1.2079 -0.9088 -0.8297 1.4176 1.9556 \n", "\n", "Coefficients: (6 not defined because of singularities)\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -0.9524636 0.2845173 -3.348 0.000815 ***\n", "schoolid2 0.0697302 0.2766287 0.252 0.800986 \n", "schoolid3 0.0382080 0.2911323 0.131 0.895586 \n", "schoolid4 0.1761334 0.2784711 0.633 0.527059 \n", "schoolid5 -0.0033389 0.2950180 -0.011 0.990970 \n", "schoolid6 0.0583548 0.3067481 0.190 0.849124 \n", "schoolid7 -0.1313759 0.3188190 -0.412 0.680288 \n", "schoolid8 0.1233661 0.3023736 0.408 0.683279 \n", "schoolid9 -0.1955428 0.3073344 -0.636 0.524611 \n", "schoolid10 -0.1892794 0.2968750 -0.638 0.523752 \n", "schoolid11 -0.2224060 0.5461005 -0.407 0.683816 \n", "schoolid12 -0.3312420 0.5414374 -0.612 0.540682 \n", "schoolid13 -0.0408540 0.3989507 -0.102 0.918436 \n", "schoolid14 -0.8681934 0.6033674 -1.439 0.150175 \n", "schoolid15 -0.1059135 0.3263162 -0.325 0.745504 \n", "schoolid16 -0.1063268 0.2885387 -0.369 0.712500 \n", "schoolid17 0.0854323 0.3119435 0.274 0.784184 \n", "schoolid18 -0.1924441 0.2997822 -0.642 0.520908 \n", "schoolid19 -0.0265326 0.3229712 -0.082 0.934526 \n", "schoolid20 -0.2179554 0.3041336 -0.717 0.473594 \n", "schoolid21 -0.2147440 0.2982822 -0.720 0.471565 \n", "schoolid22 -0.5115966 0.4410779 -1.160 0.246098 \n", "schoolid23 0.0039231 0.3475373 0.011 0.990994 \n", "schoolid24 -0.0848314 0.3259572 -0.260 0.794668 \n", "schoolid25 0.0521087 0.2754586 0.189 0.849959 \n", "schoolid26 0.0241212 0.2876511 0.084 0.933171 \n", "schoolid27 -0.2300630 0.3104796 -0.741 0.458698 \n", "schoolid28 -0.3519010 0.2924774 -1.203 0.228909 \n", "schoolid29 -0.2198764 0.3293288 -0.668 0.504357 \n", "schoolid30 -0.3146292 0.3257994 -0.966 0.334187 \n", "schoolid31 0.1398555 0.6137901 0.228 0.819759 \n", "schoolid32 0.1555524 0.3916156 0.397 0.691215 \n", "schoolid33 -0.0991693 0.3939370 -0.252 0.801243 \n", "schoolid34 -0.0073688 0.2980808 -0.025 0.980278 \n", "schoolid35 -0.3528987 0.3997273 -0.883 0.377318 \n", "schoolid36 -0.3751465 0.3988972 -0.940 0.346982 \n", "schoolid37 -0.0343169 0.3219646 -0.107 0.915117 \n", "schoolid38 -0.1346432 0.3851869 -0.350 0.726674 \n", "schoolid39 -0.4339936 0.3612869 -1.201 0.229657 \n", "schoolid40 -0.3993958 0.3834495 -1.042 0.297604 \n", "schoolid41 -0.1490784 0.3542105 -0.421 0.673846 \n", "schoolid42 -0.1545715 0.3551857 -0.435 0.663428 \n", "schoolid43 -0.5679567 0.4277455 -1.328 0.184247 \n", "schoolid44 -0.1425896 0.3774795 -0.378 0.705623 \n", "schoolid45 -0.1337888 0.3232493 -0.414 0.678957 \n", "schoolid46 -0.2573249 0.3129119 -0.822 0.410874 \n", "schoolid47 0.0027726 0.2770108 0.010 0.992014 \n", "schoolid48 -0.3406079 0.3470361 -0.981 0.326358 \n", "schoolid49 -0.3236117 0.3434541 -0.942 0.346077 \n", "schoolid50 -0.1185119 0.4086074 -0.290 0.771787 \n", "schoolid51 0.4087898 0.4506822 0.907 0.364382 \n", "schoolid52 -0.3144014 0.4118342 -0.763 0.445214 \n", "schoolid53 -0.2733677 0.4511280 -0.606 0.544538 \n", "schoolid54 -0.0889588 0.3872532 -0.230 0.818311 \n", "schoolid55 -0.1558106 0.4155020 -0.375 0.707665 \n", "schoolid56 0.1050353 0.3149235 0.334 0.738737 \n", "schoolid57 -0.0314901 0.2901719 -0.109 0.913581 \n", "schoolid58 -0.0383183 0.2730077 -0.140 0.888379 \n", "schoolid59 -0.0529637 0.2934895 -0.180 0.856790 \n", "schoolid60 -0.1624792 0.3972885 -0.409 0.682561 \n", "schoolid61 -0.0289549 0.3201953 -0.090 0.927946 \n", "schoolid62 0.0993158 0.2669678 0.372 0.709882 \n", "schoolid63 0.1684702 0.3282167 0.513 0.607749 \n", "schoolid64 -0.0693060 0.2770896 -0.250 0.802493 \n", "schoolid65 -0.0004197 0.4072922 -0.001 0.999178 \n", "schoolid66 -0.2130911 0.2984091 -0.714 0.475171 \n", "schoolid67 0.0358440 0.2921158 0.123 0.902341 \n", "schoolid68 -0.0871303 0.3290814 -0.265 0.791188 \n", "schoolid69 -0.2550387 0.2908992 -0.877 0.380636 \n", "schoolid70 -0.0268947 0.4032160 -0.067 0.946820 \n", "schoolid71 0.0037464 0.4268290 0.009 0.992997 \n", "schoolid72 -0.1304085 0.2881512 -0.453 0.650859 \n", "schoolid73 -0.2160697 0.2840030 -0.761 0.446776 \n", "schoolid74 -0.0935320 0.2842612 -0.329 0.742129 \n", "schoolid75 -0.1056241 0.3024204 -0.349 0.726892 \n", "schoolid76 -0.1052261 0.2939262 -0.358 0.720342 \n", "S3 0.1036077 0.0197345 5.250 1.52e-07 ***\n", "C1 -0.0015919 0.0053900 -0.295 0.767728 \n", "C2 -0.1038596 0.0424020 -2.449 0.014309 * \n", "C3 -0.1319218 0.0461833 -2.856 0.004284 ** \n", "XC NA NA NA NA \n", "X1 NA NA NA NA \n", "X2 NA NA NA NA \n", "X3 NA NA NA NA \n", "X4 NA NA NA NA \n", "X5 NA NA NA NA \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 13115 on 10390 degrees of freedom\n", "Residual deviance: 13009 on 10311 degrees of freedom\n", "AIC: 13169\n", "\n", "Number of Fisher Scoring iterations: 4\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# It appears that school ID does not affect pscore. So ignore it\n", "# in modeling, and just treat it as source of per-cluster error.\n", "w.lm = glm(Z ~ ., data = data.all[,-3], family = binomial)\n", "summary(w.lm)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:03:03.682359Z", "start_time": "2021-05-22T06:03:03.611Z" } }, "outputs": [], "source": [ "W = DF$Z\n", "Y = DF$Y\n", "X.raw = DF[,-(1:2)]\n", "\n", "C1.exp = model.matrix(~ factor(X.raw$C1) + 0)\n", "XC.exp = model.matrix(~ factor(X.raw$XC) + 0)\n", "\n", "X = cbind(X.raw[,-which(names(X.raw) %in% c(\"C1\", \"XC\"))], C1.exp, XC.exp)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:03:42.728244Z", "start_time": "2021-05-22T06:03:19.410Z" } }, "outputs": [], "source": [ "#\n", "# Grow a forest. Add extra trees for the causal forest.\n", "#\n", "\n", "Y.forest = regression_forest(X, Y, clusters = school.id, equalize.cluster.weights = TRUE)\n", "Y.hat = predict(Y.forest)$predictions\n", "W.forest = regression_forest(X, W, clusters = school.id, equalize.cluster.weights = TRUE)\n", "W.hat = predict(W.forest)$predictions\n", "\n", "cf.raw = causal_forest(X, Y, W,\n", " Y.hat = Y.hat, W.hat = W.hat,\n", " clusters = school.id,\n", " equalize.cluster.weights = TRUE)\n", "varimp = variable_importance(cf.raw)\n", "selected.idx = which(varimp > mean(varimp))\n", "\n", "cf = causal_forest(X[,selected.idx], Y, W,\n", " Y.hat = Y.hat, W.hat = W.hat,\n", " clusters = school.id,\n", " equalize.cluster.weights = TRUE,\n", " tune.parameters = \"all\")\n", "tau.hat = predict(cf)$predictions\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:03:54.333365Z", "start_time": "2021-05-22T06:03:54.273Z" } }, "outputs": [ { "data": { "text/html": [ "'95% CI for the ATE: 0.249 +/- 0.04'" ], "text/latex": [ "'95\\% CI for the ATE: 0.249 +/- 0.04'" ], "text/markdown": [ "'95% CI for the ATE: 0.249 +/- 0.04'" ], "text/plain": [ "[1] \"95% CI for the ATE: 0.249 +/- 0.04\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# Estimate ATE\n", "#\n", "\n", "ATE = average_treatment_effect(cf)\n", "paste(\"95% CI for the ATE:\", round(ATE[1], 3),\n", " \"+/-\", round(qnorm(0.975) * ATE[2], 3))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:04:11.203350Z", "start_time": "2021-05-22T06:04:10.027Z" } }, "outputs": [ { "data": { "text/plain": [ "\n", "Best linear fit using forest predictions (on held-out data)\n", "as well as the mean forest prediction as regressors, along\n", "with one-sided heteroskedasticity-robust (HC3) SEs:\n", "\n", " Estimate Std. Error t value Pr(>t) \n", "mean.forest.prediction 1.008054 0.082129 12.2741 <2e-16 ***\n", "differential.forest.prediction -0.552783 1.063927 -0.5196 0.6983 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "'95% CI for difference in ATE: 0.01 +/- 0.074'" ], "text/latex": [ "'95\\% CI for difference in ATE: 0.01 +/- 0.074'" ], "text/markdown": [ "'95% CI for difference in ATE: 0.01 +/- 0.074'" ], "text/plain": [ "[1] \"95% CI for difference in ATE: 0.01 +/- 0.074\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# Omnibus tests for heterogeneity\n", "#\n", "\n", "# Run best linear predictor analysis\n", "test_calibration(cf)\n", "\n", "# Compare regions with high and low estimated CATEs\n", "high_effect = tau.hat > median(tau.hat)\n", "ate.high = average_treatment_effect(cf, subset = high_effect)\n", "ate.low = average_treatment_effect(cf, subset = !high_effect)\n", "paste(\"95% CI for difference in ATE:\",\n", " round(ate.high[1] - ate.low[1], 3), \"+/-\",\n", " round(qnorm(0.975) * sqrt(ate.high[2]^2 + ate.low[2]^2), 3))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:04:51.247677Z", "start_time": "2021-05-22T06:04:51.176Z" } }, "outputs": [ { "data": { "text/plain": [ "\n", "\tWelch Two Sample t-test\n", "\n", "data: school.score[high.X1] and school.score[!high.X1]\n", "t = -3.0347, df = 71.45, p-value = 0.003357\n", "alternative hypothesis: true difference in means is not equal to 0\n", "95 percent confidence interval:\n", " -0.19218352 -0.03978437\n", "sample estimates:\n", "mean of x mean of y \n", "0.1908525 0.3068365 \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\n", "\tWelch Two Sample t-test\n", "\n", "data: school.score[high.X2] and school.score[!high.X2]\n", "t = 0.9637, df = 72.286, p-value = 0.3384\n", "alternative hypothesis: true difference in means is not equal to 0\n", "95 percent confidence interval:\n", " -0.04146936 0.11909811\n", "sample estimates:\n", "mean of x mean of y \n", "0.2682517 0.2294373 \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " Df Sum Sq Mean Sq F value Pr(>F)\n", "school.X2.levels 2 0.0811 0.04054 1.328 0.271\n", "Residuals 73 2.2283 0.03052 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# formal test for X1 and X2\n", "#\n", "\n", "dr.score = tau.hat + W / cf$W.hat *\n", " (Y - cf$Y.hat - (1 - cf$W.hat) * tau.hat) -\n", " (1 - W) / (1 - cf$W.hat) * (Y - cf$Y.hat + cf$W.hat * tau.hat)\n", "school.score = t(school.mat) %*% dr.score / school.size\n", "\n", "school.X1 = t(school.mat) %*% X$X1 / school.size\n", "high.X1 = school.X1 > median(school.X1)\n", "t.test(school.score[high.X1], school.score[!high.X1])\n", "\n", "school.X2 = (t(school.mat) %*% X$X2) / school.size\n", "high.X2 = school.X2 > median(school.X2)\n", "t.test(school.score[high.X2], school.score[!high.X2])\n", "\n", "school.X2.levels = cut(school.X2,\n", " breaks = c(-Inf, quantile(school.X2, c(1/3, 2/3)), Inf))\n", "summary(aov(school.score ~ school.X2.levels))\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:05:26.912126Z", "start_time": "2021-05-22T06:05:26.775Z" } }, "outputs": [ { "data": { "text/plain": [ "\n", "\tOne Sample t-test\n", "\n", "data: school.score.XS3.high - school.score.XS3.low\n", "t = 2.2397, df = 75, p-value = 0.02807\n", "alternative hypothesis: true mean is not equal to 0\n", "95 percent confidence interval:\n", " 0.009408619 0.160803922\n", "sample estimates:\n", " mean of x \n", "0.08510627 \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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EF9BAgQIECAwMoJ/FPWqOcp9wiTNyT9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\nm1oBN0jRtF9oe9d4w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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# formal test for S3\n", "#\n", "\n", "school.score.XS3.high = t(school.mat) %*% (dr.score * (X$S3 >= 6)) /\n", " t(school.mat) %*% (X$S3 >= 6)\n", "school.score.XS3.low = t(school.mat) %*% (dr.score * (X$S3 < 6)) /\n", " t(school.mat) %*% (X$S3 < 6)\n", "\n", "plot(school.score.XS3.low, school.score.XS3.high)\n", "t.test(school.score.XS3.high - school.score.XS3.low)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:06:54.472027Z", "start_time": "2021-05-22T06:06:54.421Z" } }, "outputs": [ { "data": { "image/png": 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2twBAgQIECAAAECBAjMI6BAmkfLvgQIECBAgAABAgQIDFpAgTTo6TU4AgQIECBA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t9MO4o36Mv6oZbG5PdS+SP7GoOE7pPtKF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", "text/plain": [ "Plot with title “”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# Look at school-wise heterogeneity\n", "#\n", "\n", "#pdf(\"school_hist.pdf\")\n", "pardef = par(mar = c(5, 4, 4, 2) + 0.5, cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)\n", "hist(school.score, xlab = \"School Treatment Effect Estimate\", main = \"\")\n", "#dev.off()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:07:07.142959Z", "start_time": "2021-05-22T06:07:07.124Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"0.249 +/- 0.039\"\n" ] } ], "source": [ "#\n", "# Re-check ATE... sanity check only\n", "#\n", "\n", "ate.hat = mean(school.score)\n", "se.hat = sqrt(var(school.score) / length(school.score - 1))\n", "print(paste(round(ate.hat, 3), \"+/-\", round(1.96 * se.hat, 3)))\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:07:26.793710Z", "start_time": "2021-05-22T06:07:26.691Z" } }, "outputs": [ { "data": { "image/png": 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LotzvYQQAABBBBAAAEEsipwnyq2XlYrR726I0AHqTvObAUBBBBAAAEEEEAg+wIj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Ag8qQIAABCNQnMDyv0sNb57ZQeKEZ87/yeKUyx/mK9+8TxUvnZsxKDAQgAAEIQGCA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\nIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQ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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "# Look at variation in propensity scores\n", "#\n", "\n", "DF = X\n", "DF$W.hat = cf$W.hat\n", "\n", "#pdf(\"pscore.pdf\")\n", "pardef = par(mar = c(5, 4, 4, 2) + 0.5, cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)\n", "boxplot(W.hat ~ S3, data = DF, ylab = \"Propensity Score\", xlab = \"Student Expectation of Success\")\n", "lines(smooth.spline(X$S3, cf$W.hat), lwd = 2, col = 4)\n", "#dev.off()\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-05-22T06:11:05.375395Z", "start_time": "2021-05-22T06:07:49.035Z" } }, "outputs": [ { "data": { "text/html": [ "'95% CI for the ATE: 0.254 +/- 0.022'" ], "text/latex": [ "'95\\% CI for the ATE: 0.254 +/- 0.022'" ], "text/markdown": [ "'95% CI for the ATE: 0.254 +/- 0.022'" ], "text/plain": [ "[1] \"95% CI for the ATE: 0.254 +/- 0.022\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\n", "Best linear fit using forest predictions (on held-out data)\n", "as well as the mean forest prediction as regressors, along\n", "with one-sided heteroskedasticity-robust (HC3) SEs:\n", "\n", " Estimate Std. Error t value Pr(>t) \n", "mean.forest.prediction 1.012757 0.045084 22.4639 < 2.2e-16 ***\n", "differential.forest.prediction 0.528275 0.133245 3.9647 3.699e-05 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 1\n", "[1] 2\n", "[1] 3\n", "[1] 4\n", "[1] 5\n" ] }, { "data": { "text/plain": [ "\n", "Best linear fit using forest predictions (on held-out data)\n", "as well as the mean forest prediction as regressors, along\n", "with one-sided heteroskedasticity-robust (HC3) SEs:\n", "\n", " Estimate Std. Error t value Pr(>t) \n", "mean.forest.prediction 0.988134 0.064817 15.2450 <2e-16 ***\n", "differential.forest.prediction 0.224634 0.213643 1.0514 0.1465 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "